Paper IDtitleauthors' keywordsFull RQsRQs-Research focuslearning-constructRaw dataFeature levelPattern levelAnalytical techniqueInsight about learningyearAuthors
1A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning TacticsLearning analytics; Learning tactic; Process model; Self-regulated learningRQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2021Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning TacticsLearning analytics; Learning tactic; Process model; Self-regulated learningRQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceProcess.miningLearning.indicators2021Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning TacticsLearning analytics; Learning tactic; Process model; Self-regulated learningRQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceCluster.analysisLearning.indicators2021Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning TacticsLearning analytics; Learning tactic; Process model; Self-regulated learningRQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2021Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning TacticsLearning analytics; Learning tactic; Process model; Self-regulated learningRQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceProcess.miningLearning.indicators2021Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning TacticsLearning analytics; Learning tactic; Process model; Self-regulated learningRQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceCluster.analysisLearning.indicators2021Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning TacticsLearning analytics; Learning tactic; Process model; Self-regulated learningRQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?Exploring.srl.processesSRLLms.log.dataTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2021Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning TacticsLearning analytics; Learning tactic; Process model; Self-regulated learningRQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?Exploring.srl.processesSRLLms.log.dataTrace-quizEvent.sequenceProcess.miningLearning.indicators2021Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning TacticsLearning analytics; Learning tactic; Process model; Self-regulated learningRQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?Exploring.srl.processesSRLLms.log.dataTrace-quizEvent.sequenceCluster.analysisLearning.indicators2021Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning TacticsLearning analytics; Learning tactic; Process model; Self-regulated learningRQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?Exploring.srl.processesSRLLms.log.dataTrace-videoEvent.sequenceFrequent.sequence.miningLearning.indicators2021Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning TacticsLearning analytics; Learning tactic; Process model; Self-regulated learningRQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?Exploring.srl.processesSRLLms.log.dataTrace-videoEvent.sequenceProcess.miningLearning.indicators2021Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning TacticsLearning analytics; Learning tactic; Process model; Self-regulated learningRQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?Exploring.srl.processesSRLLms.log.dataTrace-videoEvent.sequenceCluster.analysisLearning.indicators2021Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning TacticsLearning analytics; Learning tactic; Process model; Self-regulated learningRQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?Exploring.srl.processesSRLLms.log.dataTrace-forumEvent.sequenceFrequent.sequence.miningLearning.indicators2021Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning TacticsLearning analytics; Learning tactic; Process model; Self-regulated learningRQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?Exploring.srl.processesSRLLms.log.dataTrace-forumEvent.sequenceProcess.miningLearning.indicators2021Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning TacticsLearning analytics; Learning tactic; Process model; Self-regulated learningRQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?Exploring.srl.processesSRLLms.log.dataTrace-forumEvent.sequenceCluster.analysisLearning.indicators2021Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning TacticsLearning analytics; Learning tactic; Process model; Self-regulated learningRQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?Exploring.srl.processesSRLLms.log.dataTrace-otherEvent.sequenceFrequent.sequence.miningLearning.indicators2021Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning TacticsLearning analytics; Learning tactic; Process model; Self-regulated learningRQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?Exploring.srl.processesSRLLms.log.dataTrace-otherEvent.sequenceProcess.miningLearning.indicators2021Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
1A Learning Analytic Approach to Unveiling Self-Regulatory Processes in Learning TacticsLearning analytics; Learning tactic; Process model; Self-regulated learningRQ1: How are micro-level SRL processes activated during enactment of learning tactics in terms of frequency of their occurrence and temporal sequencing? RQ2: How do learning tactics compare in terms of frequency of occurrence and temporal sequencing of micro-level SRL processes that are activated during their enactment?Exploring.srl.processesSRLLms.log.dataTrace-otherEvent.sequenceCluster.analysisLearning.indicators2021Fan, Yizhou, Saint, John, Singh, Shaveen, Jovanovic, Jelena, Gavsevic, Dragan
2Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differencesBehavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels?Non-srl.indicators.identificationotherLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2021Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differencesBehavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels?Non-srl.indicators.identificationotherLms.log.dataTrace-quizTransitional.patternProcess.miningLearning.indicators2021Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differencesBehavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels?Non-srl.indicators.identificationotherLms.log.dataTrace-videoTransitional.patternProcess.miningLearning.indicators2021Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differencesBehavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels?Non-srl.indicators.identificationotherLms.log.dataTrace-otherTransitional.patternProcess.miningLearning.indicators2021Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differencesBehavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels?Non-srl.indicators.identificationotherLms.log.dataTrace-feedbackTransitional.patternProcess.miningLearning.indicators2021Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differencesBehavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels?Group.comparisonotherLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2021Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differencesBehavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels?Group.comparisonotherLms.log.dataTrace-quizTransitional.patternProcess.miningLearning.indicators2021Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differencesBehavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels?Group.comparisonotherLms.log.dataTrace-videoTransitional.patternProcess.miningLearning.indicators2021Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differencesBehavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels?Group.comparisonotherLms.log.dataTrace-otherTransitional.patternProcess.miningLearning.indicators2021Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
2Knowledge-construction behaviors in a mobile learning environment: a lag-sequential analysis of group differencesBehavioral patterns; Knowledge construction; Lag-sequential analysis; Mobile serious games1. What knowledge-construction behaviors did the sampled elementary-school students adopt during mobile serious game playing? 2. How and how much did the sampled students’ knowledge-construction behaviors differ across academic performance levels?Group.comparisonotherLms.log.dataTrace-feedbackTransitional.patternProcess.miningLearning.indicators2021Sun, Zhong, Lin, Chin Hsi, Lv, Kaiyue, Song, Jie
3Timing of Support in One-on-one Math Problem Solving Coaching: A Survival Analysis Approach with Multimodal DataHuman tutoring studies; Learning analtyics; MultimodalHow does the timing of support vary by problem-solving stages? How does the timing of support vary by parent-child dyads? How can the timing of support be explained by the contigency such as students’ affective/cognitive process?Time.to.interventionotherMultimodalEventSummativeBasic.statistical.analysisTime.on.learning2021Chen, Lujie Karen
3Timing of Support in One-on-one Math Problem Solving Coaching: A Survival Analysis Approach with Multimodal DataHuman tutoring studies; Learning analtyics; MultimodalHow does the timing of support vary by problem-solving stages? How does the timing of support vary by parent-child dyads? How can the timing of support be explained by the contigency such as students’ affective/cognitive process?Time.to.interventionotherMultimodalTrace-feedbackSummativeBasic.statistical.analysisTime.on.learning2021Chen, Lujie Karen
3Timing of Support in One-on-one Math Problem Solving Coaching: A Survival Analysis Approach with Multimodal DataHuman tutoring studies; Learning analtyics; MultimodalHow does the timing of support vary by problem-solving stages? How does the timing of support vary by parent-child dyads? How can the timing of support be explained by the contigency such as students’ affective/cognitive process?Time.to.interventionotherMultimodalTimeSummativeBasic.statistical.analysisTime.on.learning2021Chen, Lujie Karen
4Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine LearningPrecision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR)“How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?”Exploring.socio-dynamicscontext costumizationContextualTrace-otherGroup.event.patternContent.analysisFeedback2021Lee, Alwyn Vwen Yen
4Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine LearningPrecision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR)“How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?”Exploring.socio-dynamicscontext costumizationContextualTrace-otherGroup.event.patternContent.analysisCollaboration2021Lee, Alwyn Vwen Yen
4Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine LearningPrecision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR)“How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?”Exploring.socio-dynamicscontext costumizationContextualTrace-otherGroup.event.patternNetwork.analysisFeedback2021Lee, Alwyn Vwen Yen
4Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine LearningPrecision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR)“How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?”Exploring.socio-dynamicscontext costumizationContextualTrace-otherGroup.event.patternNetwork.analysisCollaboration2021Lee, Alwyn Vwen Yen
4Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine LearningPrecision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR)“How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?”Exploring.socio-dynamicscontext costumizationContextualTrace-otherGroup.event.patternCluster.analysisFeedback2021Lee, Alwyn Vwen Yen
4Determining Quality and Distribution of Ideas in Online Classroom Talk using Learning Analytics and Machine LearningPrecision education; Machine learning; Learning analytics; Idea Identification and Analysis (I2A); Idea Progress Reports (IPR)“How can learning analytics, machine learning, and Idea Progress Reports be used for determining the quality and distribution of ideas in different classroom talks to inform personalized interventions?”Exploring.socio-dynamicscontext costumizationContextualTrace-otherGroup.event.patternCluster.analysisCollaboration2021Lee, Alwyn Vwen Yen
5Using process mining to analyse self-regulated learning: a systematic analysis of four algorithmsLearning Analytics; Learning analytics; Micro-level Process Analysis; Micro-level process analysis; Process Mining; Process.mining; Self-Regulated Learning; Self-regulated learningRQ1: What insights can be obtained from commonly used process mining algorithms when applied in the analysis of temporal and sequential relationships of micro-level processes of SRL extracted from digital trace data?RQ2: What insights can be obtained from interpreting a combination of metrics from the commonly used PM algorithms in the analysis of micro-level processes of SRL extracted from digital trace data?Method.developmentSRLLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2021Saint, John, Fan, Yizhou, Singh, Shaveen, Gasevic, Dragan, Pardo, Abelardo
5Using process mining to analyse self-regulated learning: a systematic analysis of four algorithmsLearning Analytics; Learning analytics; Micro-level Process Analysis; Micro-level process analysis; Process Mining; Process.mining; Self-Regulated Learning; Self-regulated learningRQ1: What insights can be obtained from commonly used process mining algorithms when applied in the analysis of temporal and sequential relationships of micro-level processes of SRL extracted from digital trace data?RQ2: What insights can be obtained from interpreting a combination of metrics from the commonly used PM algorithms in the analysis of micro-level processes of SRL extracted from digital trace data?Method.developmentSRLLms.log.dataTrace-readingTransitional.patternProcess.miningLearning.indicators2021Saint, John, Fan, Yizhou, Singh, Shaveen, Gasevic, Dragan, Pardo, Abelardo
5Using process mining to analyse self-regulated learning: a systematic analysis of four algorithmsLearning Analytics; Learning analytics; Micro-level Process Analysis; Micro-level process analysis; Process Mining; Process.mining; Self-Regulated Learning; Self-regulated learningRQ1: What insights can be obtained from commonly used process mining algorithms when applied in the analysis of temporal and sequential relationships of micro-level processes of SRL extracted from digital trace data?RQ2: What insights can be obtained from interpreting a combination of metrics from the commonly used PM algorithms in the analysis of micro-level processes of SRL extracted from digital trace data?Method.developmentSRLLms.log.dataTrace-videoTransitional.patternProcess.miningLearning.indicators2021Saint, John, Fan, Yizhou, Singh, Shaveen, Gasevic, Dragan, Pardo, Abelardo
5Using process mining to analyse self-regulated learning: a systematic analysis of four algorithmsLearning Analytics; Learning analytics; Micro-level Process Analysis; Micro-level process analysis; Process Mining; Process.mining; Self-Regulated Learning; Self-regulated learningRQ1: What insights can be obtained from commonly used process mining algorithms when applied in the analysis of temporal and sequential relationships of micro-level processes of SRL extracted from digital trace data?RQ2: What insights can be obtained from interpreting a combination of metrics from the commonly used PM algorithms in the analysis of micro-level processes of SRL extracted from digital trace data?Method.developmentSRLLms.log.dataTrace-forumTransitional.patternProcess.miningLearning.indicators2021Saint, John, Fan, Yizhou, Singh, Shaveen, Gasevic, Dragan, Pardo, Abelardo
6SAINT+: Integrating Temporal Features for EdNet Correctness PredictionDeep Learning; Education; Knowledge Tracing; Personalized Learning; TransformerNoneMethod.developmentNoneLms.log.dataEventNoneNeural.networkCourse.design2021Shin, Dongmin, Shim, Yugeun, Yu, Hangyeol, Lee, Seewoo, Kim, Byungsoo, Choi, Youngduck
6SAINT+: Integrating Temporal Features for EdNet Correctness PredictionDeep Learning; Education; Knowledge Tracing; Personalized Learning; TransformerNoneMethod.developmentNoneLms.log.dataTimeNoneNeural.networkCourse.design2021Shin, Dongmin, Shim, Yugeun, Yu, Hangyeol, Lee, Seewoo, Kim, Byungsoo, Choi, Youngduck
7Understanding learner behaviour in online courses with Bayesian modelling and time series characterisationBayesian analysis; Learning; Online instructionNoneMethod.developmentotherLms.log.dataEventTransitional.patternProcess.miningCourse.design2021Peach, Robert L, Greenbury, Sam F, Johnston, Iain G, Yaliraki, Sophia N, Lefevre, David J, Barahona, Mauricio
7Understanding learner behaviour in online courses with Bayesian modelling and time series characterisationBayesian analysis; Learning; Online instructionNoneMethod.developmentotherLms.log.dataTrace-readingTransitional.patternProcess.miningCourse.design2021Peach, Robert L, Greenbury, Sam F, Johnston, Iain G, Yaliraki, Sophia N, Lefevre, David J, Barahona, Mauricio
7Understanding learner behaviour in online courses with Bayesian modelling and time series characterisationBayesian analysis; Learning; Online instructionNoneMethod.developmentotherLms.log.dataTrace-quizTransitional.patternProcess.miningCourse.design2021Peach, Robert L, Greenbury, Sam F, Johnston, Iain G, Yaliraki, Sophia N, Lefevre, David J, Barahona, Mauricio
7Understanding learner behaviour in online courses with Bayesian modelling and time series characterisationBayesian analysis; Learning; Online instructionNoneMethod.developmentotherLms.log.dataTrace-forumTransitional.patternProcess.miningCourse.design2021Peach, Robert L, Greenbury, Sam F, Johnston, Iain G, Yaliraki, Sophia N, Lefevre, David J, Barahona, Mauricio
8Temporality revisited: Dynamicity issues in collaborative digital writing researchCollaborative digital writing; Conceptual learning; Feedback; Higher education; Knowledge constructionWhat are the underlying elements of current and technological research in CDW? And: Are there flaws or neglected aspects and what would an improved methodology look like?Method.developmentcollaborative knowledge buildingLearning.productTrace-forumNoneContent.analysisCollaboration2021Engerer, Volkmar P.
8Temporality revisited: Dynamicity issues in collaborative digital writing researchCollaborative digital writing; Conceptual learning; Feedback; Higher education; Knowledge constructionWhat are the underlying elements of current and technological research in CDW? And: Are there flaws or neglected aspects and what would an improved methodology look like?Method.developmentcollaborative knowledge buildingLearning.productTrace-forumNoneContent.analysisLearning.indicators2021Engerer, Volkmar P.
9Temporal Cross-Effects in Knowledge Tracingcollaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effectswe want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects)Method.developmentknowledge tracingPerformance.measuresEventOther.sequential.patternsProcess.miningTime.on.learning2021Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping
9Temporal Cross-Effects in Knowledge Tracingcollaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effectswe want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects)Method.developmentknowledge tracingPerformance.measuresEventTransitional.patternProcess.miningTime.on.learning2021Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping
9Temporal Cross-Effects in Knowledge Tracingcollaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effectswe want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects)Method.developmentknowledge tracingPerformance.measuresTimeOther.sequential.patternsProcess.miningTime.on.learning2021Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping
9Temporal Cross-Effects in Knowledge Tracingcollaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effectswe want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects)Method.developmentknowledge tracingPerformance.measuresTimeTransitional.patternProcess.miningTime.on.learning2021Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping
9Temporal Cross-Effects in Knowledge Tracingcollaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effectswe want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects)Method.developmentknowledge tracingPerformance.measuresTrace-quizOther.sequential.patternsProcess.miningTime.on.learning2021Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping
9Temporal Cross-Effects in Knowledge Tracingcollaborative filtering; educational data mining; hawkes process; knowledge tracing; temporal cross-effectswe want to address that learning is a dynamic process and there exist temporal cross-effects in KT. For one thing, the mastery ofa skill is not only influenced by previous interactions of the same skill, but also the others (cross-effects)Method.developmentknowledge tracingPerformance.measuresTrace-quizTransitional.patternProcess.miningTime.on.learning2021Wang, Chenyang, Ma, Weizhi, Zhang, Min, Lv, Chuancheng, Wan, Fengyuan, Lin, Huijie, Tang, Taoran, Liu, Yiqun, Ma, Shaoping
10Theory-based learning analytics to explore student engagement patterns in a peer review activityPeer reviews; learning analytics; process mining; student engagementHow can theory-informed LA help identify and interpret engagement patterns in peer reviews?Exploring.srl.processesSRL; SSRLLms.log.dataEventTransitional.patternProcess.miningCollaboration2021Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10Theory-based learning analytics to explore student engagement patterns in a peer review activityPeer reviews; learning analytics; process mining; student engagementHow can theory-informed LA help identify and interpret engagement patterns in peer reviews?Exploring.srl.processesSRL; SSRLLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2021Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10Theory-based learning analytics to explore student engagement patterns in a peer review activityPeer reviews; learning analytics; process mining; student engagementHow can theory-informed LA help identify and interpret engagement patterns in peer reviews?Exploring.srl.processesSRL; SSRLLms.log.dataTrace-readingTransitional.patternProcess.miningCollaboration2021Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10Theory-based learning analytics to explore student engagement patterns in a peer review activityPeer reviews; learning analytics; process mining; student engagementHow can theory-informed LA help identify and interpret engagement patterns in peer reviews?Exploring.srl.processesSRL; SSRLLms.log.dataTrace-readingTransitional.patternProcess.miningLearning.indicators2021Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10Theory-based learning analytics to explore student engagement patterns in a peer review activityPeer reviews; learning analytics; process mining; student engagementHow can theory-informed LA help identify and interpret engagement patterns in peer reviews?Exploring.srl.processesSRL; SSRLLms.log.dataTrace-forumTransitional.patternProcess.miningCollaboration2021Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10Theory-based learning analytics to explore student engagement patterns in a peer review activityPeer reviews; learning analytics; process mining; student engagementHow can theory-informed LA help identify and interpret engagement patterns in peer reviews?Exploring.srl.processesSRL; SSRLLms.log.dataTrace-forumTransitional.patternProcess.miningLearning.indicators2021Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10Theory-based learning analytics to explore student engagement patterns in a peer review activityPeer reviews; learning analytics; process mining; student engagementHow can theory-informed LA help identify and interpret engagement patterns in peer reviews?Exploring.srl.processesSRL; SSRLLearning.productEventTransitional.patternProcess.miningCollaboration2021Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10Theory-based learning analytics to explore student engagement patterns in a peer review activityPeer reviews; learning analytics; process mining; student engagementHow can theory-informed LA help identify and interpret engagement patterns in peer reviews?Exploring.srl.processesSRL; SSRLLearning.productEventTransitional.patternProcess.miningLearning.indicators2021Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10Theory-based learning analytics to explore student engagement patterns in a peer review activityPeer reviews; learning analytics; process mining; student engagementHow can theory-informed LA help identify and interpret engagement patterns in peer reviews?Exploring.srl.processesSRL; SSRLLearning.productTrace-readingTransitional.patternProcess.miningCollaboration2021Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10Theory-based learning analytics to explore student engagement patterns in a peer review activityPeer reviews; learning analytics; process mining; student engagementHow can theory-informed LA help identify and interpret engagement patterns in peer reviews?Exploring.srl.processesSRL; SSRLLearning.productTrace-readingTransitional.patternProcess.miningLearning.indicators2021Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10Theory-based learning analytics to explore student engagement patterns in a peer review activityPeer reviews; learning analytics; process mining; student engagementHow can theory-informed LA help identify and interpret engagement patterns in peer reviews?Exploring.srl.processesSRL; SSRLLearning.productTrace-forumTransitional.patternProcess.miningCollaboration2021Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
10Theory-based learning analytics to explore student engagement patterns in a peer review activityPeer reviews; learning analytics; process mining; student engagementHow can theory-informed LA help identify and interpret engagement patterns in peer reviews?Exploring.srl.processesSRL; SSRLLearning.productTrace-forumTransitional.patternProcess.miningLearning.indicators2021Er, Erkan, Villa-Torrano, Cristina, Dimitriadis, Yannis, Gasevic, Dragan, Bote-Lorenzo, Miguel L, Asensio-Perez, Juan I, Gomez-Sanchez, Eduardo, {Mart\'\inez Mones}, Alejandra
11Slow is good: the effect of diligence on student performance in the case of an adaptive learning system for health literacyadaptive e-learning system; Cluster analysis ; differentiation; diversity; health literacy; learning analytics; reading competenceRQ1: Is temporal behavior a differentiator between students?RQ2: Is temporal behavior correlated with performance?Group.comparisoncontext costumizationCustomized.log.dataEventSummativeCluster.analysisTime.on.learning2021Fadljevic, Leon, Maitz, Katharina, Kowald, Dominik, Pammer-Schindler, Viktoria, Gasteiger-Klicpera, Barbara
11Slow is good: the effect of diligence on student performance in the case of an adaptive learning system for health literacyadaptive e-learning system; Cluster analysis ; differentiation; diversity; health literacy; learning analytics; reading competenceRQ1: Is temporal behavior a differentiator between students?RQ2: Is temporal behavior correlated with performance?Group.comparisoncontext costumizationCustomized.log.dataTimeSummativeCluster.analysisTime.on.learning2021Fadljevic, Leon, Maitz, Katharina, Kowald, Dominik, Pammer-Schindler, Viktoria, Gasteiger-Klicpera, Barbara
11Slow is good: the effect of diligence on student performance in the case of an adaptive learning system for health literacyadaptive e-learning system; Cluster analysis ; differentiation; diversity; health literacy; learning analytics; reading competenceRQ1: Is temporal behavior a differentiator between students?RQ2: Is temporal behavior correlated with performance?Group.comparisoncontext costumizationPerformance.measuresEventSummativeCluster.analysisTime.on.learning2021Fadljevic, Leon, Maitz, Katharina, Kowald, Dominik, Pammer-Schindler, Viktoria, Gasteiger-Klicpera, Barbara
11Slow is good: the effect of diligence on student performance in the case of an adaptive learning system for health literacyadaptive e-learning system; Cluster analysis ; differentiation; diversity; health literacy; learning analytics; reading competenceRQ1: Is temporal behavior a differentiator between students?RQ2: Is temporal behavior correlated with performance?Group.comparisoncontext costumizationPerformance.measuresTimeSummativeCluster.analysisTime.on.learning2021Fadljevic, Leon, Maitz, Katharina, Kowald, Dominik, Pammer-Schindler, Viktoria, Gasteiger-Klicpera, Barbara
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLms.log.dataEventTransitional.patternCluster.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLms.log.dataEventTransitional.patternProcess.miningNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLms.log.dataEventTransitional.patternVisualization.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLms.log.dataEventSummativeCluster.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLms.log.dataEventSummativeProcess.miningNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLms.log.dataEventSummativeVisualization.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLms.log.dataTimeTransitional.patternCluster.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLms.log.dataTimeTransitional.patternProcess.miningNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLms.log.dataTimeTransitional.patternVisualization.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLms.log.dataTimeSummativeCluster.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLms.log.dataTimeSummativeProcess.miningNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLms.log.dataTimeSummativeVisualization.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLearning.productEventTransitional.patternCluster.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLearning.productEventTransitional.patternProcess.miningNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLearning.productEventTransitional.patternVisualization.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLearning.productEventSummativeCluster.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLearning.productEventSummativeProcess.miningNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLearning.productEventSummativeVisualization.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLearning.productTimeTransitional.patternCluster.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLearning.productTimeTransitional.patternProcess.miningNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLearning.productTimeTransitional.patternVisualization.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLearning.productTimeSummativeCluster.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLearning.productTimeSummativeProcess.miningNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?Group.comparisonNoneLearning.productTimeSummativeVisualization.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLms.log.dataEventTransitional.patternCluster.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLms.log.dataEventTransitional.patternProcess.miningNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLms.log.dataEventTransitional.patternVisualization.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLms.log.dataEventSummativeCluster.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLms.log.dataEventSummativeProcess.miningNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLms.log.dataEventSummativeVisualization.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLms.log.dataTimeTransitional.patternCluster.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLms.log.dataTimeTransitional.patternProcess.miningNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLms.log.dataTimeTransitional.patternVisualization.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLms.log.dataTimeSummativeCluster.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLms.log.dataTimeSummativeProcess.miningNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLms.log.dataTimeSummativeVisualization.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLearning.productEventTransitional.patternCluster.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLearning.productEventTransitional.patternProcess.miningNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLearning.productEventTransitional.patternVisualization.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLearning.productEventSummativeCluster.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLearning.productEventSummativeProcess.miningNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLearning.productEventSummativeVisualization.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLearning.productTimeTransitional.patternCluster.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLearning.productTimeTransitional.patternProcess.miningNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLearning.productTimeTransitional.patternVisualization.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLearning.productTimeSummativeCluster.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLearning.productTimeSummativeProcess.miningNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
12Unfolding Students’ Online Assignment Submission Behavioral Patterns using Temporal Learning AnalyticsAcademic achievement; Analysis; At risk students; Clustering; Colleges & universities; Data mining; Distance learning; Education; Educational evaluation; Homework; Machine learning; Markov chains; Mathematical analysis; Online education; Special Issue Articles; Student behavior; Usage; assignment submission behavior; educational data mining; learning performance; precision education; temporal learning analyticsRQ1. What are the students’ behavioral patterns of online assignment submission? RQ2. How do students’ behavioral patterns of online assignment submission change over time? RQ3. What are the association rules between students’ online assignment submission behaviors and their learning performance that can be used to predict at-risk students as early as possible?At-risk.student.identificationNoneLearning.productTimeSummativeVisualization.analysisNo.learning.focus.outcome2021Koko, Mehmet, Akapƒnar, Gokhan, Hasnine, Mohammad Nehal, Kokoc, Mehmet, Akcapinar, Gokhan, Hasnine, Mohammad Nehal
13A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning AnalyticsKnowledge acquisition; Medical students; MemoryOur research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.Non-srl.indicators.identificationotherLearning.productTrace-videoSummativeQualitative.analysisCourse.design2021McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning AnalyticsKnowledge acquisition; Medical students; MemoryOur research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.Non-srl.indicators.identificationotherLearning.productTrace-videoSummativeQualitative.analysisFeedback2021McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning AnalyticsKnowledge acquisition; Medical students; MemoryOur research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.Non-srl.indicators.identificationotherLearning.productTrace-videoSummativeVisualization.analysisCourse.design2021McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning AnalyticsKnowledge acquisition; Medical students; MemoryOur research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.Non-srl.indicators.identificationotherLearning.productTrace-videoSummativeVisualization.analysisFeedback2021McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning AnalyticsKnowledge acquisition; Medical students; MemoryOur research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.Non-srl.indicators.identificationotherLearning.productTrace-feedbackSummativeQualitative.analysisCourse.design2021McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning AnalyticsKnowledge acquisition; Medical students; MemoryOur research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.Non-srl.indicators.identificationotherLearning.productTrace-feedbackSummativeQualitative.analysisFeedback2021McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning AnalyticsKnowledge acquisition; Medical students; MemoryOur research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.Non-srl.indicators.identificationotherLearning.productTrace-feedbackSummativeVisualization.analysisCourse.design2021McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning AnalyticsKnowledge acquisition; Medical students; MemoryOur research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.Non-srl.indicators.identificationotherLearning.productTrace-feedbackSummativeVisualization.analysisFeedback2021McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning AnalyticsKnowledge acquisition; Medical students; MemoryOur research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.Non-srl.indicators.identificationotherLearning.productTrace-otherSummativeQualitative.analysisCourse.design2021McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning AnalyticsKnowledge acquisition; Medical students; MemoryOur research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.Non-srl.indicators.identificationotherLearning.productTrace-otherSummativeQualitative.analysisFeedback2021McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning AnalyticsKnowledge acquisition; Medical students; MemoryOur research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.Non-srl.indicators.identificationotherLearning.productTrace-otherSummativeVisualization.analysisCourse.design2021McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
13A Random Controlled Trial to Examine the Efficacy of Blank Slate: A Novel Spaced Retrieval Tool with Real-Time Learning AnalyticsKnowledge acquisition; Medical students; MemoryOur research objective was to examine the efficacy of Blank Slate to (1) offset normal human forgetting; (2) unobtrusively monitor learner progress; and (3) create a detailed data record, computationally analyzed to display helpful feedback on individual learner performance.Non-srl.indicators.identificationotherLearning.productTrace-otherSummativeVisualization.analysisFeedback2021McHugh, Douglas, Feinn, Richard, McIlvenna, Jeff, Trevithick, Matt
14Time-driven modeling of student self-regulated learning in Network analysis-based tutorsSelf-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?Exploring.srl.processesSRLLms.log.dataEventSummativeCluster.analysisCourse.design2021Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14Time-driven modeling of student self-regulated learning in Network analysis-based tutorsSelf-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?Exploring.srl.processesSRLLms.log.dataEventSummativeCluster.analysisTime.on.learning2021Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14Time-driven modeling of student self-regulated learning in Network analysis-based tutorsSelf-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?Exploring.srl.processesSRLLms.log.dataTrace-readingSummativeCluster.analysisCourse.design2021Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14Time-driven modeling of student self-regulated learning in Network analysis-based tutorsSelf-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?Exploring.srl.processesSRLLms.log.dataTrace-readingSummativeCluster.analysisTime.on.learning2021Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14Time-driven modeling of student self-regulated learning in Network analysis-based tutorsSelf-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?Exploring.srl.processesSRLLms.log.dataTimeSummativeCluster.analysisCourse.design2021Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14Time-driven modeling of student self-regulated learning in Network analysis-based tutorsSelf-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?Exploring.srl.processesSRLLms.log.dataTimeSummativeCluster.analysisTime.on.learning2021Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14Time-driven modeling of student self-regulated learning in Network analysis-based tutorsSelf-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?Exploring.srl.processesSRLSelf-reportedEventSummativeCluster.analysisCourse.design2021Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14Time-driven modeling of student self-regulated learning in Network analysis-based tutorsSelf-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?Exploring.srl.processesSRLSelf-reportedEventSummativeCluster.analysisTime.on.learning2021Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14Time-driven modeling of student self-regulated learning in Network analysis-based tutorsSelf-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?Exploring.srl.processesSRLSelf-reportedTrace-readingSummativeCluster.analysisCourse.design2021Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14Time-driven modeling of student self-regulated learning in Network analysis-based tutorsSelf-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?Exploring.srl.processesSRLSelf-reportedTrace-readingSummativeCluster.analysisTime.on.learning2021Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14Time-driven modeling of student self-regulated learning in Network analysis-based tutorsSelf-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?Exploring.srl.processesSRLSelf-reportedTimeSummativeCluster.analysisCourse.design2021Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
14Time-driven modeling of student self-regulated learning in Network analysis-based tutorsSelf-regulated learning; Network analysis-based tutors; open- ended learning environments; scaffolding; time-driven modeling(a) Are there distinct profiles of student behaviors, indicative of SRL processes in the information and acquisition phase, across experimental conditions?(b) Can students’ time-derived metrics from each profile of SRL processes, establishing the latency, duration, and sequence of SRL processes in the information seeking and acquisition phase, predict learning outcomes across experimental conditions?Exploring.srl.processesSRLSelf-reportedTimeSummativeCluster.analysisTime.on.learning2021Poitras, Eric G., Doleck, Tenzin, Huang, Lingyun, Dias, Laurel, Lajoie, Susanne P.
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventTransitional.patternBasic.statistical.analysisLearning.indicators2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventTransitional.patternBasic.statistical.analysisCollaboration2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventTransitional.patternProcess.miningCollaboration2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventSummativeBasic.statistical.analysisLearning.indicators2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventSummativeBasic.statistical.analysisCollaboration2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventSummativeProcess.miningLearning.indicators2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventSummativeProcess.miningCollaboration2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternBasic.statistical.analysisLearning.indicators2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternBasic.statistical.analysisCollaboration2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternProcess.miningLearning.indicators2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternProcess.miningCollaboration2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumSummativeBasic.statistical.analysisLearning.indicators2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumSummativeBasic.statistical.analysisCollaboration2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumSummativeProcess.miningLearning.indicators2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumSummativeProcess.miningCollaboration2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingSelf-reportedEventTransitional.patternBasic.statistical.analysisLearning.indicators2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingSelf-reportedEventTransitional.patternBasic.statistical.analysisCollaboration2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingSelf-reportedEventTransitional.patternProcess.miningLearning.indicators2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingSelf-reportedEventTransitional.patternProcess.miningCollaboration2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingSelf-reportedEventSummativeBasic.statistical.analysisLearning.indicators2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingSelf-reportedEventSummativeBasic.statistical.analysisCollaboration2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingSelf-reportedEventSummativeProcess.miningLearning.indicators2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingSelf-reportedEventSummativeProcess.miningCollaboration2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingSelf-reportedTrace-forumTransitional.patternBasic.statistical.analysisLearning.indicators2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingSelf-reportedTrace-forumTransitional.patternBasic.statistical.analysisCollaboration2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingSelf-reportedTrace-forumTransitional.patternProcess.miningLearning.indicators2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingSelf-reportedTrace-forumTransitional.patternProcess.miningCollaboration2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingSelf-reportedTrace-forumSummativeBasic.statistical.analysisLearning.indicators2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingSelf-reportedTrace-forumSummativeBasic.statistical.analysisCollaboration2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingSelf-reportedTrace-forumSummativeProcess.miningLearning.indicators2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
15Computer-supported collaborative concept mapping: the impact of students’ perceptions of collaboration on their knowledge understanding and behavioral patternsConcept mapping; computer-supported collaborative learning; factual knowledge understanding; conceptual knowledge understanding; behavioral patternHypothesis 1. Students with a high level of collaborative perception will demonstrate better factual and conceptual knowledge understanding than those with a low level of collaborative perception.Hypothesis 2. Students with a high level of collaborative perception will demonstrate more diverse behavioral transition sequences than those with a low level of collaborative perception.Exploring.socio-dynamicscollaborative knowledge buildingSelf-reportedTrace-forumSummativeProcess.miningCollaboration2021Liu, Sannyuya, Kang, Lingyun, Liu, Zhi, Fang, Jing, Yang, Zongkai, Sun, Jianwen, Wang, Meiyi, Hu, Mengwei
16Using process mining for Git log analysis of projects in a software development courseComputer Appl. in Social and Behavioral Sciences; Computers and Education; Education; Educational Technology; Information Systems Applications (incl.Internet); User Interfaces and Human Computer Interaction; generalRQ1: what are the features to extract from the Git log data, and how should be data be processed in order to be usable in the process mining analysis of project development? RQ2: what are the characteristics of the project development process form the perpective of the Git log attributes? RQ3: what are the benefits and limitation of process mining in the Git log analysis of student projects?Method.developmentNoneCustomized.log.dataEventTransitional.patternProcess.miningNo.learning.focus.outcome2021Macak, Martin, Kruzelova, Daniela, Chren, Stanislav, Buhnova, Barbora
17Variational Deep Knowledge Tracing for Language Learningdeep learning; knowledge tracing; language learning; student modeling; variational inferenceNoneMethod.developmentNoneCustomized.log.dataEventNoneNeural.networkCourse.design2021Ruan, Sherry, Wei, Wei, Landay, James
17Variational Deep Knowledge Tracing for Language Learningdeep learning; knowledge tracing; language learning; student modeling; variational inferenceNoneMethod.developmentNoneCustomized.log.dataEventNoneVisualization.analysisCourse.design2021Ruan, Sherry, Wei, Wei, Landay, James
17Variational Deep Knowledge Tracing for Language Learningdeep learning; knowledge tracing; language learning; student modeling; variational inferenceNoneMethod.developmentNoneCustomized.log.dataTimeNoneNeural.networkCourse.design2021Ruan, Sherry, Wei, Wei, Landay, James
17Variational Deep Knowledge Tracing for Language Learningdeep learning; knowledge tracing; language learning; student modeling; variational inferenceNoneMethod.developmentNoneCustomized.log.dataTimeNoneVisualization.analysisCourse.design2021Ruan, Sherry, Wei, Wei, Landay, James
17Variational Deep Knowledge Tracing for Language Learningdeep learning; knowledge tracing; language learning; student modeling; variational inferenceNoneMethod.developmentNoneLearning.productEventNoneNeural.networkCourse.design2021Ruan, Sherry, Wei, Wei, Landay, James
17Variational Deep Knowledge Tracing for Language Learningdeep learning; knowledge tracing; language learning; student modeling; variational inferenceNoneMethod.developmentNoneLearning.productEventNoneVisualization.analysisCourse.design2021Ruan, Sherry, Wei, Wei, Landay, James
17Variational Deep Knowledge Tracing for Language Learningdeep learning; knowledge tracing; language learning; student modeling; variational inferenceNoneMethod.developmentNoneLearning.productTimeNoneNeural.networkCourse.design2021Ruan, Sherry, Wei, Wei, Landay, James
17Variational Deep Knowledge Tracing for Language Learningdeep learning; knowledge tracing; language learning; student modeling; variational inferenceNoneMethod.developmentNoneLearning.productTimeNoneVisualization.analysisCourse.design2021Ruan, Sherry, Wei, Wei, Landay, James
18Towards Mutual Theory of Mind in Human-AI Interaction: How Language Reflects What Students Perceive About a Virtual Teaching Assistantconversational agent; human-AI interaction; language analysis; online community; online education; theory of mindRQ 1: How does a community’s perception of a community-facing CA change over time?RQ 2: How do linguistic markers of human-AI interaction refect perception about the community-facing CA?Non-srl.indicators.identificationotherSelf-reportedTrace-forumSummativeBasic.statistical.analysisLearning.indicators2021Wang, Qiaosi, Saha, Koustuv, Gregori, Eric, Joyner, David, Goel, Ashok
18Towards Mutual Theory of Mind in Human-AI Interaction: How Language Reflects What Students Perceive About a Virtual Teaching Assistantconversational agent; human-AI interaction; language analysis; online community; online education; theory of mindRQ 1: How does a community’s perception of a community-facing CA change over time?RQ 2: How do linguistic markers of human-AI interaction refect perception about the community-facing CA?Method.developmentotherSelf-reportedTrace-forumSummativeBasic.statistical.analysisLearning.indicators2021Wang, Qiaosi, Saha, Koustuv, Gregori, Eric, Joyner, David, Goel, Ashok
19Using Marginal Models to Adjust for Statistical Bias in the Analysis of State TransitionsL Basic statistical analysisistic; affect dynamics; marginal models; sequential data; transition metricsaddressing the problem of inflated values in finding significance in transitionsMethod.developmentaffective learningLms.log.dataEventTransitional.patternProcess.miningNo.learning.focus.outcome2021Matayoshi, Jeffrey, Karumbaiah, Shamya
19Using Marginal Models to Adjust for Statistical Bias in the Analysis of State TransitionsL Basic statistical analysisistic; affect dynamics; marginal models; sequential data; transition metricsaddressing the problem of inflated values in finding significance in transitionsMethod.developmentaffective learningLms.log.dataTrace-otherTransitional.patternProcess.miningNo.learning.focus.outcome2021Matayoshi, Jeffrey, Karumbaiah, Shamya
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLms.log.dataEventTransitional.patternBasic.statistical.analysisLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLms.log.dataEventTransitional.patternVisualization.analysisLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLms.log.dataTrace-readingTransitional.patternProcess.miningLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLms.log.dataTrace-readingTransitional.patternBasic.statistical.analysisLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLms.log.dataTrace-readingTransitional.patternVisualization.analysisLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLms.log.dataTrace-feedbackTransitional.patternProcess.miningLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLms.log.dataTrace-feedbackTransitional.patternBasic.statistical.analysisLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLms.log.dataTrace-feedbackTransitional.patternVisualization.analysisLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLms.log.dataTrace-otherTransitional.patternProcess.miningLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLms.log.dataTrace-otherTransitional.patternBasic.statistical.analysisLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLms.log.dataTrace-otherTransitional.patternVisualization.analysisLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLearning.productEventTransitional.patternProcess.miningLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLearning.productEventTransitional.patternBasic.statistical.analysisLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLearning.productEventTransitional.patternVisualization.analysisLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLearning.productTrace-readingTransitional.patternProcess.miningLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLearning.productTrace-readingTransitional.patternBasic.statistical.analysisLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLearning.productTrace-readingTransitional.patternVisualization.analysisLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLearning.productTrace-feedbackTransitional.patternProcess.miningLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLearning.productTrace-feedbackTransitional.patternBasic.statistical.analysisLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLearning.productTrace-feedbackTransitional.patternVisualization.analysisLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLearning.productTrace-otherTransitional.patternProcess.miningLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLearning.productTrace-otherTransitional.patternBasic.statistical.analysisLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
20Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematicsApplications in subject areas; Interactive learning environments; Pedagogical issues; Secondary education; Teaching/learning strategies(1). Are there any significant differences in the learning achievements of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(2). Are there any significant differences in the attitude toward learning mathematics of students who adopted a social regulation- based online learning framework or a conventional self-regulated learning framework to perform the learning activities?(3). Are there any significant differences in the behavioral patterns of students who adopted a social regulation-based online learning framework or a conventional self-regulated learning framework to learn and interact?Exploring.srl.processesSRL; SSRLLearning.productTrace-otherTransitional.patternVisualization.analysisLearning.indicators2021Hwang, Gwo-Jen, Wang, Sheng-Yuan, Lai, Chiu-Lin
21Towards the successful game-based learning: Detection and feedback to misconceptions is the keyElementary education; Games; Teaching/learning strategies(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?Non-srl.indicators.identificationgame-based learningCustomized.log.dataEventTransitional.patternProcess.miningFeedback2021Yang, Kai-Hsiang, Lu, Bou-Chuan
21Towards the successful game-based learning: Detection and feedback to misconceptions is the keyElementary education; Games; Teaching/learning strategies(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?Non-srl.indicators.identificationgame-based learningCustomized.log.dataEventTransitional.patternBasic.statistical.analysisFeedback2021Yang, Kai-Hsiang, Lu, Bou-Chuan
21Towards the successful game-based learning: Detection and feedback to misconceptions is the keyElementary education; Games; Teaching/learning strategies(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?Non-srl.indicators.identificationgame-based learningCustomized.log.dataTrace-otherTransitional.patternProcess.miningFeedback2021Yang, Kai-Hsiang, Lu, Bou-Chuan
21Towards the successful game-based learning: Detection and feedback to misconceptions is the keyElementary education; Games; Teaching/learning strategies(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?Non-srl.indicators.identificationgame-based learningCustomized.log.dataTrace-otherTransitional.patternBasic.statistical.analysisFeedback2021Yang, Kai-Hsiang, Lu, Bou-Chuan
21Towards the successful game-based learning: Detection and feedback to misconceptions is the keyElementary education; Games; Teaching/learning strategies(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?Non-srl.indicators.identificationgame-based learningCustomized.log.dataTrace-feedbackTransitional.patternProcess.miningFeedback2021Yang, Kai-Hsiang, Lu, Bou-Chuan
21Towards the successful game-based learning: Detection and feedback to misconceptions is the keyElementary education; Games; Teaching/learning strategies(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?Non-srl.indicators.identificationgame-based learningCustomized.log.dataTrace-feedbackTransitional.patternBasic.statistical.analysisFeedback2021Yang, Kai-Hsiang, Lu, Bou-Chuan
21Towards the successful game-based learning: Detection and feedback to misconceptions is the keyElementary education; Games; Teaching/learning strategies(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?Non-srl.indicators.identificationgame-based learningLearning.productEventTransitional.patternProcess.miningFeedback2021Yang, Kai-Hsiang, Lu, Bou-Chuan
21Towards the successful game-based learning: Detection and feedback to misconceptions is the keyElementary education; Games; Teaching/learning strategies(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?Non-srl.indicators.identificationgame-based learningLearning.productEventTransitional.patternBasic.statistical.analysisFeedback2021Yang, Kai-Hsiang, Lu, Bou-Chuan
21Towards the successful game-based learning: Detection and feedback to misconceptions is the keyElementary education; Games; Teaching/learning strategies(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?Non-srl.indicators.identificationgame-based learningLearning.productTrace-otherTransitional.patternProcess.miningFeedback2021Yang, Kai-Hsiang, Lu, Bou-Chuan
21Towards the successful game-based learning: Detection and feedback to misconceptions is the keyElementary education; Games; Teaching/learning strategies(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?Non-srl.indicators.identificationgame-based learningLearning.productTrace-otherTransitional.patternBasic.statistical.analysisFeedback2021Yang, Kai-Hsiang, Lu, Bou-Chuan
21Towards the successful game-based learning: Detection and feedback to misconceptions is the keyElementary education; Games; Teaching/learning strategies(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?Non-srl.indicators.identificationgame-based learningLearning.productTrace-feedbackTransitional.patternProcess.miningFeedback2021Yang, Kai-Hsiang, Lu, Bou-Chuan
21Towards the successful game-based learning: Detection and feedback to misconceptions is the keyElementary education; Games; Teaching/learning strategies(1). Do students using the game-based learning model with two-tier testing present better learning effectiveness than those using a general game-based learning model? (2). Do students using the game-based learning model with two-tier testing show greater improvement in terms of mathematics anxiety than those using a general game-based learning model? (3). What learning behavior patterns do students using the game-based learning model with two-tier testing display?Non-srl.indicators.identificationgame-based learningLearning.productTrace-feedbackTransitional.patternBasic.statistical.analysisFeedback2021Yang, Kai-Hsiang, Lu, Bou-Chuan
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceCluster.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceProcess.miningLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceVisualization.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLms.log.dataEventTransitional.patternCluster.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLms.log.dataEventTransitional.patternVisualization.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceCluster.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceProcess.miningLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceVisualization.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLms.log.dataTrace-readingTransitional.patternCluster.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLms.log.dataTrace-readingTransitional.patternProcess.miningLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLms.log.dataTrace-readingTransitional.patternVisualization.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLms.log.dataTrace-otherEvent.sequenceCluster.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLms.log.dataTrace-otherEvent.sequenceProcess.miningLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLms.log.dataTrace-otherEvent.sequenceVisualization.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLms.log.dataTrace-otherTransitional.patternCluster.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLms.log.dataTrace-otherTransitional.patternProcess.miningLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLms.log.dataTrace-otherTransitional.patternVisualization.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLearning.productEventEvent.sequenceCluster.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLearning.productEventEvent.sequenceProcess.miningLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLearning.productEventEvent.sequenceVisualization.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLearning.productEventTransitional.patternCluster.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLearning.productEventTransitional.patternProcess.miningLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLearning.productEventTransitional.patternVisualization.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLearning.productTrace-readingEvent.sequenceCluster.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLearning.productTrace-readingEvent.sequenceProcess.miningLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLearning.productTrace-readingEvent.sequenceVisualization.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLearning.productTrace-readingTransitional.patternCluster.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLearning.productTrace-readingTransitional.patternProcess.miningLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLearning.productTrace-readingTransitional.patternVisualization.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLearning.productTrace-otherEvent.sequenceCluster.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLearning.productTrace-otherEvent.sequenceProcess.miningLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLearning.productTrace-otherEvent.sequenceVisualization.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLearning.productTrace-otherTransitional.patternCluster.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLearning.productTrace-otherTransitional.patternProcess.miningLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
22Process analysis of teachers’ self-regulated learning patterns in technological pedagogical content knowledge developmentLog files; Process.mining; Self-regulated learning; TPACK1) what global SRL process patterns can be identified in terms of different levels of TPACK achievements?(2) what within-group SRL process patterns can be identified in terms of the high and the low TPACK performers with individual TPACK groups?Exploring.srl.processesSRLLearning.productTrace-otherTransitional.patternVisualization.analysisLearning.indicators2021Huang, Lingyun, Lajoie, Susanne P
23Predicting student success in a blended learning environmentblended learning; e-learning; feature extraction; grade prediction; learning analytics; logistic regression; machine learning; random forest classificationNoneAt-risk.student.identificationNoneLms.log.dataEventSummativeBasic.statistical.analysisNo.learning.focus.outcome2020Van Goidsenhoven}, Steven, Bogdanova, Daria, Deeva, Galina, vanden Broucke, Seppe, {De Weerdt}, Jochen, Snoeck, Monique
23Predicting student success in a blended learning environmentblended learning; e-learning; feature extraction; grade prediction; learning analytics; logistic regression; machine learning; random forest classificationNoneAt-risk.student.identificationNoneLms.log.dataTimeSummativeBasic.statistical.analysisNo.learning.focus.outcome2020Van Goidsenhoven}, Steven, Bogdanova, Daria, Deeva, Galina, vanden Broucke, Seppe, {De Weerdt}, Jochen, Snoeck, Monique
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataEventEvent.sequenceProcess.miningLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataEventEvent.sequenceCluster.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataEventEvent.sequenceVisualization.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataEventGroup.event.patternProcess.miningLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataEventGroup.event.patternCluster.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataEventGroup.event.patternVisualization.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataTrace-readingEvent.sequenceProcess.miningLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataTrace-readingEvent.sequenceCluster.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataTrace-readingEvent.sequenceVisualization.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataTrace-readingGroup.event.patternProcess.miningLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataTrace-readingGroup.event.patternCluster.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataTrace-readingGroup.event.patternVisualization.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataTrace-quizEvent.sequenceProcess.miningLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataTrace-quizEvent.sequenceCluster.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataTrace-quizEvent.sequenceVisualization.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataTrace-quizGroup.event.patternProcess.miningLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataTrace-quizGroup.event.patternCluster.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataTrace-quizGroup.event.patternVisualization.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataTrace-forumEvent.sequenceProcess.miningLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataTrace-forumEvent.sequenceCluster.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataTrace-forumEvent.sequenceVisualization.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataTrace-forumGroup.event.patternProcess.miningLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataTrace-forumGroup.event.patternCluster.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Method.developmentcognitive activities (learning actions)Lms.log.dataTrace-forumGroup.event.patternVisualization.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataEventEvent.sequenceProcess.miningLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataEventEvent.sequenceCluster.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataEventEvent.sequenceVisualization.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataEventGroup.event.patternProcess.miningLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataEventGroup.event.patternCluster.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataEventGroup.event.patternVisualization.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataTrace-readingEvent.sequenceProcess.miningLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataTrace-readingEvent.sequenceCluster.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataTrace-readingEvent.sequenceVisualization.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataTrace-readingGroup.event.patternProcess.miningLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataTrace-readingGroup.event.patternCluster.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataTrace-readingGroup.event.patternVisualization.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataTrace-quizEvent.sequenceProcess.miningLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataTrace-quizEvent.sequenceCluster.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataTrace-quizEvent.sequenceVisualization.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataTrace-quizGroup.event.patternProcess.miningLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataTrace-quizGroup.event.patternCluster.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataTrace-quizGroup.event.patternVisualization.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataTrace-forumEvent.sequenceProcess.miningLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataTrace-forumEvent.sequenceCluster.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataTrace-forumEvent.sequenceVisualization.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataTrace-forumGroup.event.patternProcess.miningLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataTrace-forumGroup.event.patternCluster.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
24Supporting actionable intelligence: reframing the analysis of observed study strategiesexplanatory models; learner behaviour; learning analytics; learning tactics and strategies; trace dataRQ1: Can learning trace data be used to extract a comprehensive set of features for early-in-the-course (i.e. after 2-3 weeks) detection of study strategies that (i) are predictive of course grades, and (ii) offer rich information about student learning behaviour? RQ2: Do students from different performance tiers (high and low) differ in how they adopt observed study strategies throughout the course?Group.comparisoncognitive activities (learning actions)Lms.log.dataTrace-forumGroup.event.patternVisualization.analysisLearning.indicators2020Jovanovic, Jelena, Dawson, Shane, Joksimovic, Srecko, Siemens, George
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataEventEvent.sequenceProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataEventEvent.sequenceCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataEventEvent.sequenceNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataEventEvent.sequenceVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataEventGroup.event.patternProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataEventGroup.event.patternCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataEventGroup.event.patternNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataEventGroup.event.patternVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataEventTransitional.patternProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataEventTransitional.patternCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataEventTransitional.patternNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataEventTransitional.patternVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-readingEvent.sequenceProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-readingEvent.sequenceCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-readingEvent.sequenceNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-readingEvent.sequenceVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-readingGroup.event.patternProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-readingGroup.event.patternCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-readingGroup.event.patternNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-readingGroup.event.patternVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-readingTransitional.patternProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-readingTransitional.patternCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-readingTransitional.patternNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-readingTransitional.patternVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-quizEvent.sequenceProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-quizEvent.sequenceCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-quizEvent.sequenceNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-quizEvent.sequenceVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-quizGroup.event.patternProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-quizGroup.event.patternCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-quizGroup.event.patternNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-quizGroup.event.patternVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-quizTransitional.patternProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-quizTransitional.patternCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-quizTransitional.patternNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTrace-quizTransitional.patternVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTimeEvent.sequenceProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTimeEvent.sequenceCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTimeEvent.sequenceNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTimeEvent.sequenceVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTimeGroup.event.patternProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTimeGroup.event.patternCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTimeGroup.event.patternNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTimeGroup.event.patternVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTimeTransitional.patternProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTimeTransitional.patternCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTimeTransitional.patternNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLLms.log.dataTimeTransitional.patternVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresEventEvent.sequenceProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresEventEvent.sequenceCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresEventEvent.sequenceNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresEventEvent.sequenceVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresEventGroup.event.patternProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresEventGroup.event.patternCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresEventGroup.event.patternNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresEventGroup.event.patternVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresEventTransitional.patternProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresEventTransitional.patternCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresEventTransitional.patternNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresEventTransitional.patternVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-readingEvent.sequenceProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-readingEvent.sequenceCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-readingEvent.sequenceNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-readingEvent.sequenceVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-readingGroup.event.patternProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-readingGroup.event.patternCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-readingGroup.event.patternNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-readingGroup.event.patternVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-readingTransitional.patternProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-readingTransitional.patternCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-readingTransitional.patternNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-readingTransitional.patternVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-quizEvent.sequenceProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-quizEvent.sequenceCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-quizEvent.sequenceNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-quizEvent.sequenceVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-quizGroup.event.patternProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-quizGroup.event.patternCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-quizGroup.event.patternNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-quizGroup.event.patternVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-quizTransitional.patternProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-quizTransitional.patternCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-quizTransitional.patternNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTrace-quizTransitional.patternVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTimeEvent.sequenceProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTimeEvent.sequenceCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTimeEvent.sequenceNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTimeEvent.sequenceVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTimeGroup.event.patternProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTimeGroup.event.patternCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTimeGroup.event.patternNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTimeGroup.event.patternVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTimeTransitional.patternProcess.miningTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTimeTransitional.patternCluster.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTimeTransitional.patternNetwork.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
25Analytics of time management and learning strategies for effective online learning in blended environmentsblended learning; learning analytics; learning strategies; self-regulated learning; time management strategiesRQ1: To what extent can a combination of data analytic methods provide a holistic view to theoretically meaningful learning strate- gies composed of time management and learning tactics? RQ2: To what extent a combination of Network analysis and process ana- lytics techniques, proposed in this study, can be used to explain the critical dimensions (i.e., time, ordering, frequency, and strength of connections in tactic use) of learning strategies extracted from trace data?Method.developmentSRLPerformance.measuresTimeTransitional.patternVisualization.analysisTime.on.learning2020Uzir, Nora'ayu Ahmad, Gavsevic, Dragan, Jovanovic, Jelena, Matcha, Wannisa, Lim, Lisa-Angelique, Fudge, Anthea
26How 'Networked' are Online Collaborative Concept-Maps? Introducing Metrics for Quantifying and Comparing the 'Networkedness' of Collaboratively Constructed Contentcollaborative learning; online collaboration; online discussion; assessment of collaboration in learning; Network analysis analysis; concept mappingNoneMethod.developmentcollaborative knowledge buildingLearning.productEventSummativeNetwork.analysisCollaboration2020Sher, Noa, Kent, Carmel, Rafaeli, Sheizaf
26How 'Networked' are Online Collaborative Concept-Maps? Introducing Metrics for Quantifying and Comparing the 'Networkedness' of Collaboratively Constructed Contentcollaborative learning; online collaboration; online discussion; assessment of collaboration in learning; Network analysis analysis; concept mappingNoneMethod.developmentcollaborative knowledge buildingLearning.productEventSummativeNetwork.analysisLearning.indicators2020Sher, Noa, Kent, Carmel, Rafaeli, Sheizaf
26How 'Networked' are Online Collaborative Concept-Maps? Introducing Metrics for Quantifying and Comparing the 'Networkedness' of Collaboratively Constructed Contentcollaborative learning; online collaboration; online discussion; assessment of collaboration in learning; Network analysis analysis; concept mappingNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-forumSummativeNetwork.analysisCollaboration2020Sher, Noa, Kent, Carmel, Rafaeli, Sheizaf
26How 'Networked' are Online Collaborative Concept-Maps? Introducing Metrics for Quantifying and Comparing the 'Networkedness' of Collaboratively Constructed Contentcollaborative learning; online collaboration; online discussion; assessment of collaboration in learning; Network analysis analysis; concept mappingNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-forumSummativeNetwork.analysisLearning.indicators2020Sher, Noa, Kent, Carmel, Rafaeli, Sheizaf
27A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student predictionNoneNoneAt-risk.student.identificationNoneLms.log.dataEventOther.sequential.patternsNeural.networkNo.learning.focus.outcome2020Qiao, Chen, Hu, Xiao
27A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student predictionNoneNoneAt-risk.student.identificationNoneLms.log.dataEventOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2020Qiao, Chen, Hu, Xiao
27A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student predictionNoneNoneAt-risk.student.identificationNoneLms.log.dataTimeOther.sequential.patternsNeural.networkNo.learning.focus.outcome2020Qiao, Chen, Hu, Xiao
27A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student predictionNoneNoneAt-risk.student.identificationNoneLms.log.dataTimeOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2020Qiao, Chen, Hu, Xiao
27A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student predictionNoneNoneAt-risk.student.identificationNonePerformance.measuresEventOther.sequential.patternsNeural.networkNo.learning.focus.outcome2020Qiao, Chen, Hu, Xiao
27A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student predictionNoneNoneAt-risk.student.identificationNonePerformance.measuresEventOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2020Qiao, Chen, Hu, Xiao
27A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student predictionNoneNoneAt-risk.student.identificationNonePerformance.measuresTimeOther.sequential.patternsNeural.networkNo.learning.focus.outcome2020Qiao, Chen, Hu, Xiao
27A joint neural Network analysis model for combining heterogeneous user data sources: An example of at‐risk student predictionNoneNoneAt-risk.student.identificationNonePerformance.measuresTimeOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2020Qiao, Chen, Hu, Xiao
28Process.mining for self-regulated learning assessment in e-learninge-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive minerour aim is to assess students’ SRL skill during an e-Learning course through a new EPM techniqueExploring.srl.processesSRLLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2020Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28Process.mining for self-regulated learning assessment in e-learninge-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive minerour aim is to assess students’ SRL skill during an e-Learning course through a new EPM techniqueExploring.srl.processesSRLLms.log.dataEventTransitional.patternVisualization.analysisLearning.indicators2020Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28Process.mining for self-regulated learning assessment in e-learninge-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive minerour aim is to assess students’ SRL skill during an e-Learning course through a new EPM techniqueExploring.srl.processesSRLLms.log.dataTrace-readingTransitional.patternProcess.miningLearning.indicators2020Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28Process.mining for self-regulated learning assessment in e-learninge-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive minerour aim is to assess students’ SRL skill during an e-Learning course through a new EPM techniqueExploring.srl.processesSRLLms.log.dataTrace-readingTransitional.patternVisualization.analysisLearning.indicators2020Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28Process.mining for self-regulated learning assessment in e-learninge-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive minerour aim is to assess students’ SRL skill during an e-Learning course through a new EPM techniqueExploring.srl.processesSRLLms.log.dataTrace-forumTransitional.patternProcess.miningLearning.indicators2020Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28Process.mining for self-regulated learning assessment in e-learninge-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive minerour aim is to assess students’ SRL skill during an e-Learning course through a new EPM techniqueExploring.srl.processesSRLLms.log.dataTrace-forumTransitional.patternVisualization.analysisLearning.indicators2020Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28Process.mining for self-regulated learning assessment in e-learninge-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive minerour aim is to assess students’ SRL skill during an e-Learning course through a new EPM techniqueExploring.srl.processesSRLLms.log.dataTrace-quizTransitional.patternProcess.miningLearning.indicators2020Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28Process.mining for self-regulated learning assessment in e-learninge-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive minerour aim is to assess students’ SRL skill during an e-Learning course through a new EPM techniqueExploring.srl.processesSRLLms.log.dataTrace-quizTransitional.patternVisualization.analysisLearning.indicators2020Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28Process.mining for self-regulated learning assessment in e-learninge-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive minerour aim is to assess students’ SRL skill during an e-Learning course through a new EPM techniqueMethod.developmentSRLLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2020Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28Process.mining for self-regulated learning assessment in e-learninge-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive minerour aim is to assess students’ SRL skill during an e-Learning course through a new EPM techniqueMethod.developmentSRLLms.log.dataEventTransitional.patternVisualization.analysisLearning.indicators2020Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28Process.mining for self-regulated learning assessment in e-learninge-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive minerour aim is to assess students’ SRL skill during an e-Learning course through a new EPM techniqueMethod.developmentSRLLms.log.dataTrace-readingTransitional.patternProcess.miningLearning.indicators2020Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28Process.mining for self-regulated learning assessment in e-learninge-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive minerour aim is to assess students’ SRL skill during an e-Learning course through a new EPM techniqueMethod.developmentSRLLms.log.dataTrace-readingTransitional.patternVisualization.analysisLearning.indicators2020Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28Process.mining for self-regulated learning assessment in e-learninge-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive minerour aim is to assess students’ SRL skill during an e-Learning course through a new EPM techniqueMethod.developmentSRLLms.log.dataTrace-forumTransitional.patternProcess.miningLearning.indicators2020Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28Process.mining for self-regulated learning assessment in e-learninge-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive minerour aim is to assess students’ SRL skill during an e-Learning course through a new EPM techniqueMethod.developmentSRLLms.log.dataTrace-forumTransitional.patternVisualization.analysisLearning.indicators2020Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28Process.mining for self-regulated learning assessment in e-learninge-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive minerour aim is to assess students’ SRL skill during an e-Learning course through a new EPM techniqueMethod.developmentSRLLms.log.dataTrace-quizTransitional.patternProcess.miningLearning.indicators2020Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
28Process.mining for self-regulated learning assessment in e-learninge-Learning; Self-regulated learning; Educational process mining; Educational data mining ; Inductive minerour aim is to assess students’ SRL skill during an e-Learning course through a new EPM techniqueMethod.developmentSRLLms.log.dataTrace-quizTransitional.patternVisualization.analysisLearning.indicators2020Cerezo, Rebeca, Bogarin, Alejandro, Esteban, Maria, Romero, Cristobal
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataEventGroup.event.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataEventGroup.event.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataEventTransitional.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-otherEvent.sequenceProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-otherEvent.sequenceCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-otherGroup.event.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-otherGroup.event.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-otherTransitional.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-otherTransitional.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-quizEvent.sequenceProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-quizEvent.sequenceCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-quizGroup.event.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-quizGroup.event.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-quizTransitional.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-quizTransitional.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-readingGroup.event.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-readingGroup.event.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-readingTransitional.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-readingTransitional.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-otherEvent.sequenceProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-otherEvent.sequenceCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-otherGroup.event.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-otherGroup.event.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-otherTransitional.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLLms.log.dataTrace-otherTransitional.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresEventEvent.sequenceProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresEventEvent.sequenceCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresEventGroup.event.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresEventGroup.event.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresEventTransitional.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresEventTransitional.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-otherEvent.sequenceProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-otherEvent.sequenceCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-otherGroup.event.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-otherGroup.event.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-otherTransitional.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-otherTransitional.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-quizEvent.sequenceProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-quizEvent.sequenceCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-quizGroup.event.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-quizGroup.event.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-quizTransitional.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-quizTransitional.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-readingEvent.sequenceProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-readingEvent.sequenceCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-readingGroup.event.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-readingGroup.event.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-readingTransitional.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-readingTransitional.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-otherEvent.sequenceProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-otherEvent.sequenceCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-otherGroup.event.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-otherGroup.event.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-otherTransitional.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Exploring.srl.processesSRLPerformance.measuresTrace-otherTransitional.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataEventEvent.sequenceProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataEventEvent.sequenceCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataEventGroup.event.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataEventGroup.event.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataEventTransitional.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-otherEvent.sequenceProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-otherEvent.sequenceCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-otherGroup.event.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-otherGroup.event.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-otherTransitional.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-otherTransitional.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-quizEvent.sequenceProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-quizEvent.sequenceCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-quizGroup.event.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-quizGroup.event.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-quizTransitional.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-quizTransitional.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-readingEvent.sequenceProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-readingEvent.sequenceCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-readingGroup.event.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-readingGroup.event.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-readingTransitional.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-readingTransitional.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-otherEvent.sequenceProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-otherEvent.sequenceCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-otherGroup.event.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-otherGroup.event.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-otherTransitional.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLLms.log.dataTrace-otherTransitional.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresEventEvent.sequenceProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresEventEvent.sequenceCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresEventGroup.event.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresEventGroup.event.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresEventTransitional.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresEventTransitional.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-otherEvent.sequenceProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-otherEvent.sequenceCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-otherGroup.event.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-otherGroup.event.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-otherTransitional.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-otherTransitional.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-quizEvent.sequenceProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-quizEvent.sequenceCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-quizGroup.event.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-quizGroup.event.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-quizTransitional.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-quizTransitional.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-readingEvent.sequenceProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-readingEvent.sequenceCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-readingGroup.event.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-readingGroup.event.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-readingTransitional.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-readingTransitional.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-otherEvent.sequenceProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-otherEvent.sequenceCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-otherGroup.event.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-otherGroup.event.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-otherTransitional.patternProcess.miningLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
29Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace DataMarkov models; learning analytics; microlevel process analysis; process mining' self-regulated learning1) It proposes an approach for the measurement of SRL microlevel processes from digital traces collected in a common learning environment.2) It proposes a stochastic process mining (PM) approach that analyses sequences of extracted SRL microlevel processes to provide insights into the ways in which stu- dents self-regulate their learning in common learning environments.3) In a temporal and sequential context, it identifies differen- ces in SRL between high- and low-performing students.4) It outlines an approach that allows for the Qualitative analysis comparison of the SRL processes engaged by learners who followed different learning strategies.Method.developmentSRLPerformance.measuresTrace-otherTransitional.patternCluster.analysisLearning.indicators2020Saint, John, Whitelock-Wainwright, Alexander, Gasevic, Dragan, Pardo, Abelardo
30Implementing dynamicity in research designs for collaborative digital writingCollaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research designRQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice?Method.developmentcollaborative knowledge buildingLearning.productTrace-forumOther.sequential.patternsVisualization.analysisTime.on.learning2020Engerer, Volkmar P.
30Implementing dynamicity in research designs for collaborative digital writingCollaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research designRQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice?Method.developmentcollaborative knowledge buildingLearning.productTrace-forumOther.sequential.patternsVisualization.analysisLearning.indicators2020Engerer, Volkmar P.
30Implementing dynamicity in research designs for collaborative digital writingCollaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research designRQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice?Method.developmentcollaborative knowledge buildingLearning.productTrace-feedbackOther.sequential.patternsVisualization.analysisTime.on.learning2020Engerer, Volkmar P.
30Implementing dynamicity in research designs for collaborative digital writingCollaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research designRQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice?Method.developmentcollaborative knowledge buildingLearning.productTrace-feedbackOther.sequential.patternsVisualization.analysisLearning.indicators2020Engerer, Volkmar P.
30Implementing dynamicity in research designs for collaborative digital writingCollaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research designRQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice?Method.developmentcollaborative knowledge buildingSelf-reportedTrace-forumOther.sequential.patternsVisualization.analysisTime.on.learning2020Engerer, Volkmar P.
30Implementing dynamicity in research designs for collaborative digital writingCollaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research designRQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice?Method.developmentcollaborative knowledge buildingSelf-reportedTrace-forumOther.sequential.patternsVisualization.analysisLearning.indicators2020Engerer, Volkmar P.
30Implementing dynamicity in research designs for collaborative digital writingCollaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research designRQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice?Method.developmentcollaborative knowledge buildingSelf-reportedTrace-feedbackOther.sequential.patternsVisualization.analysisTime.on.learning2020Engerer, Volkmar P.
30Implementing dynamicity in research designs for collaborative digital writingCollaborative digital writing; Conceptual learning; Feedback; Google docs; Higher education; Knowledge construction; Leximancer; Research designRQ 1: What are the underlying elements of current and technological research designs in (Basic statistical analysise-of-the-art) CDW? RQ 2: Are there flaws or neglected aspects in these designs? What would an improved research design look like?RQ 3: How can a dynamized research design be implemented in practice?Method.developmentcollaborative knowledge buildingSelf-reportedTrace-feedbackOther.sequential.patternsVisualization.analysisLearning.indicators2020Engerer, Volkmar P.
31Predicting Learners' Effortful Behaviour in Adaptive Assessment Using Multimodal Dataadaptive assessment; effort classification; hidden Markov models; multimodal learning analyticsRQ: How can we predict learners’ effort using multimodal data?Method.developmentotherMultimodalEventSummativeCluster.analysisTime.on.learning2020Sharma, Kshitij, Papamitsiou, Zacharoula, Olsen, Jennifer K, Giannakos, Michail
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataEventEvent.sequenceProcess.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataEventEvent.sequenceCluster.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataEventEvent.sequenceVisualization.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataEventGroup.event.patternFrequent.sequence.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataEventGroup.event.patternProcess.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataEventGroup.event.patternCluster.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataEventGroup.event.patternVisualization.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataEventTransitional.patternFrequent.sequence.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataEventTransitional.patternCluster.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataEventTransitional.patternVisualization.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-readingEvent.sequenceProcess.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-readingEvent.sequenceCluster.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-readingEvent.sequenceVisualization.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-readingGroup.event.patternProcess.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-readingGroup.event.patternCluster.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-readingGroup.event.patternVisualization.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-readingTransitional.patternFrequent.sequence.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-readingTransitional.patternProcess.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-readingTransitional.patternCluster.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-readingTransitional.patternVisualization.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-feedbackEvent.sequenceFrequent.sequence.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-feedbackEvent.sequenceProcess.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-feedbackEvent.sequenceCluster.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-feedbackEvent.sequenceVisualization.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-feedbackGroup.event.patternFrequent.sequence.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-feedbackGroup.event.patternProcess.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-feedbackGroup.event.patternCluster.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-feedbackGroup.event.patternVisualization.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-feedbackTransitional.patternFrequent.sequence.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-feedbackTransitional.patternProcess.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-feedbackTransitional.patternCluster.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-feedbackTransitional.patternVisualization.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-quizEvent.sequenceProcess.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-quizEvent.sequenceCluster.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-quizEvent.sequenceVisualization.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-quizGroup.event.patternFrequent.sequence.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-quizGroup.event.patternProcess.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-quizGroup.event.patternCluster.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-quizGroup.event.patternVisualization.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-quizTransitional.patternFrequent.sequence.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-quizTransitional.patternProcess.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-quizTransitional.patternCluster.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-quizTransitional.patternVisualization.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherEvent.sequenceFrequent.sequence.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherEvent.sequenceProcess.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherEvent.sequenceCluster.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherEvent.sequenceVisualization.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherGroup.event.patternFrequent.sequence.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherGroup.event.patternProcess.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherGroup.event.patternCluster.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherGroup.event.patternVisualization.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherTransitional.patternFrequent.sequence.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherTransitional.patternProcess.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherTransitional.patternCluster.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherTransitional.patternVisualization.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherEvent.sequenceFrequent.sequence.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherEvent.sequenceProcess.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherEvent.sequenceCluster.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherEvent.sequenceVisualization.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherGroup.event.patternFrequent.sequence.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherGroup.event.patternProcess.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherGroup.event.patternCluster.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherGroup.event.patternVisualization.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherTransitional.patternFrequent.sequence.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherTransitional.patternProcess.miningLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherTransitional.patternCluster.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
32Analytics of Learning Strategies: Role of Course Design and Delivery ModalityLearning strategies; course design; data mining; learning tactics; modality; self-regulated learningRQ1: Given a sequence of learning actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when interacting with online learning activities across different course designs that are based on different delivery modalities? RQ2: Is there an association between learning strategies automatically detected with data analytic methods from trace data and students’ academic performance in different course designs that are based on different delivery modalities?Method.developmentSRLLms.log.dataTrace-otherTransitional.patternVisualization.analysisLearning.indicators2020Matcha, Wannisa, Gavsevic, Dragan, {Ahmad Uzir}, Nora'ayu, Jovanovic, Jelena, Pardo, Abelardo, Lim, Lisa, Maldonado-Mahauad, Jorge, Gentili, Sheridan, Perez-Sanagustin, Mar, Tsai, Yi-Shan
33Prediction of students’ early dropout based on their interaction logs in online learning environmentPrediction; extract feature; input-output hidden Markov model; logistic regression; machine learning; online learning environmentNoneAt-risk.student.identificationNoneLms.log.dataEventSummativeBasic.statistical.analysisNo.learning.focus.outcome2020Mubarak, Ahmed A., Cao, Han, Zhang, Weizhen
34Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activityGender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes?Group.comparisoncollaborative knowledge buildingLms.log.dataEventTransitional.patternProcess.miningCollaboration2020Wu, Sheng Yi, Wang, Shu Ming
34Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activityGender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes?Group.comparisoncollaborative knowledge buildingLms.log.dataEventSummativeProcess.miningCollaboration2020Wu, Sheng Yi, Wang, Shu Ming
34Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activityGender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes?Group.comparisoncollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternProcess.miningCollaboration2020Wu, Sheng Yi, Wang, Shu Ming
34Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activityGender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes?Group.comparisoncollaborative knowledge buildingLms.log.dataTrace-forumSummativeProcess.miningCollaboration2020Wu, Sheng Yi, Wang, Shu Ming
34Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activityGender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes?Non-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataEventTransitional.patternProcess.miningCollaboration2020Wu, Sheng Yi, Wang, Shu Ming
34Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activityGender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes?Non-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataEventSummativeProcess.miningCollaboration2020Wu, Sheng Yi, Wang, Shu Ming
34Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activityGender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes?Non-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternProcess.miningCollaboration2020Wu, Sheng Yi, Wang, Shu Ming
34Exploring the effects of gender grouping and the cognitive processing patterns of a Facebook-based online collaborative learning activityGender grouping; behavioural patterns; collaborative learning; online discussion; social Network analysising services1) What are the distributions of cognitive processes among the groups in the online problem- solving discussion activity? (2) What are the behavioural patterns, in terms of sequential analysis, exhibited among the groups in the online problem-solving discussion activity? (3) Do the different gender composition groups behave differently in terms of distributions and behavioural patterns of cognitive processes?Non-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTrace-forumSummativeProcess.miningCollaboration2020Wu, Sheng Yi, Wang, Shu Ming
35Reply to which post? An analysis of peer reviews in a high school SPOCPeer review; SPOC; high school; online interaction; social Network analysis analysiswhat's the characteritics of the interactive netwrok of students' peer review? Which posts are more likely to receive reviews from others? How do students who received more attention intact with other?Exploring.socio-dynamicscollaborative knowledge building; feedbackLearning.productEventSummativeBasic.statistical.analysisTime.on.learning2020Wang, Mengqian, Guo, Wenge, Le, Huixiao, Qiao, Bo
35Reply to which post? An analysis of peer reviews in a high school SPOCPeer review; SPOC; high school; online interaction; social Network analysis analysiswhat's the characteritics of the interactive netwrok of students' peer review? Which posts are more likely to receive reviews from others? How do students who received more attention intact with other?Exploring.socio-dynamicscollaborative knowledge building; feedbackLearning.productEventSummativeBasic.statistical.analysisFeedback2020Wang, Mengqian, Guo, Wenge, Le, Huixiao, Qiao, Bo
35Reply to which post? An analysis of peer reviews in a high school SPOCPeer review; SPOC; high school; online interaction; social Network analysis analysiswhat's the characteritics of the interactive netwrok of students' peer review? Which posts are more likely to receive reviews from others? How do students who received more attention intact with other?Exploring.socio-dynamicscollaborative knowledge building; feedbackLearning.productTrace-forumSummativeBasic.statistical.analysisTime.on.learning2020Wang, Mengqian, Guo, Wenge, Le, Huixiao, Qiao, Bo
35Reply to which post? An analysis of peer reviews in a high school SPOCPeer review; SPOC; high school; online interaction; social Network analysis analysiswhat's the characteritics of the interactive netwrok of students' peer review? Which posts are more likely to receive reviews from others? How do students who received more attention intact with other?Exploring.socio-dynamicscollaborative knowledge building; feedbackLearning.productTrace-forumSummativeBasic.statistical.analysisFeedback2020Wang, Mengqian, Guo, Wenge, Le, Huixiao, Qiao, Bo
36Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board GameBoard game; Computational participation; Computational thinking; UnpluggedHow did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies?Group.comparisongame-based learningCustomized.log.dataEventTransitional.patternProcess.miningLearning.indicators2020Kuo, Wei Chen, Hsu, Ting Chia
36Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board GameBoard game; Computational participation; Computational thinking; UnpluggedHow did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies?Group.comparisongame-based learningCustomized.log.dataEventTransitional.patternBasic.statistical.analysisLearning.indicators2020Kuo, Wei Chen, Hsu, Ting Chia
36Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board GameBoard game; Computational participation; Computational thinking; UnpluggedHow did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies?Group.comparisongame-based learningCustomized.log.dataTrace-forumTransitional.patternProcess.miningLearning.indicators2020Kuo, Wei Chen, Hsu, Ting Chia
36Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board GameBoard game; Computational participation; Computational thinking; UnpluggedHow did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies?Group.comparisongame-based learningCustomized.log.dataTrace-forumTransitional.patternBasic.statistical.analysisLearning.indicators2020Kuo, Wei Chen, Hsu, Ting Chia
36Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board GameBoard game; Computational participation; Computational thinking; UnpluggedHow did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies?Non-srl.indicators.identificationgame-based learningCustomized.log.dataEventTransitional.patternProcess.miningLearning.indicators2020Kuo, Wei Chen, Hsu, Ting Chia
36Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board GameBoard game; Computational participation; Computational thinking; UnpluggedHow did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies?Non-srl.indicators.identificationgame-based learningCustomized.log.dataEventTransitional.patternBasic.statistical.analysisLearning.indicators2020Kuo, Wei Chen, Hsu, Ting Chia
36Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board GameBoard game; Computational participation; Computational thinking; UnpluggedHow did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies?Non-srl.indicators.identificationgame-based learningCustomized.log.dataTrace-forumTransitional.patternProcess.miningLearning.indicators2020Kuo, Wei Chen, Hsu, Ting Chia
36Learning Computational Thinking Without a Computer: How Computational Participation Happens in a Computational Thinking Board GameBoard game; Computational participation; Computational thinking; UnpluggedHow did the students’ learning computational thinking performance differ according to the different game- based learning strategies?What are the students’ learning computational thinking behavioral patterns with the different game-based learning strategies?Non-srl.indicators.identificationgame-based learningCustomized.log.dataTrace-forumTransitional.patternBasic.statistical.analysisLearning.indicators2020Kuo, Wei Chen, Hsu, Ting Chia
37Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approachLearning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamicsHow do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?Exploring.srl.processesSRLCustomized.log.dataEventTransitional.patternNetwork.analysisLearning.indicators2020Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approachLearning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamicsHow do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?Exploring.srl.processesSRLCustomized.log.dataEventTransitional.patternBasic.statistical.analysisLearning.indicators2020Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approachLearning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamicsHow do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?Exploring.srl.processesSRLCustomized.log.dataEventSummativeNetwork.analysisLearning.indicators2020Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approachLearning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamicsHow do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?Exploring.srl.processesSRLCustomized.log.dataEventSummativeBasic.statistical.analysisLearning.indicators2020Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approachLearning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamicsHow do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?Exploring.srl.processesSRLCustomized.log.dataTrace-otherTransitional.patternNetwork.analysisLearning.indicators2020Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approachLearning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamicsHow do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?Exploring.srl.processesSRLCustomized.log.dataTrace-otherTransitional.patternBasic.statistical.analysisLearning.indicators2020Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approachLearning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamicsHow do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?Exploring.srl.processesSRLCustomized.log.dataTrace-otherSummativeNetwork.analysisLearning.indicators2020Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approachLearning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamicsHow do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?Exploring.srl.processesSRLCustomized.log.dataTrace-otherSummativeBasic.statistical.analysisLearning.indicators2020Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approachLearning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamicsHow do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?Exploring.srl.processesSRLSelf-reportedEventTransitional.patternNetwork.analysisLearning.indicators2020Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approachLearning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamicsHow do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?Exploring.srl.processesSRLSelf-reportedEventTransitional.patternBasic.statistical.analysisLearning.indicators2020Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approachLearning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamicsHow do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?Exploring.srl.processesSRLSelf-reportedEventSummativeNetwork.analysisLearning.indicators2020Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approachLearning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamicsHow do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?Exploring.srl.processesSRLSelf-reportedEventSummativeBasic.statistical.analysisLearning.indicators2020Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approachLearning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamicsHow do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?Exploring.srl.processesSRLSelf-reportedTrace-otherTransitional.patternNetwork.analysisLearning.indicators2020Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approachLearning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamicsHow do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?Exploring.srl.processesSRLSelf-reportedTrace-otherTransitional.patternBasic.statistical.analysisLearning.indicators2020Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approachLearning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamicsHow do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?Exploring.srl.processesSRLSelf-reportedTrace-otherSummativeNetwork.analysisLearning.indicators2020Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
37Examining temporal dynamics of self-regulated learning behaviors in STEM learning: A Network analysis approachLearning analytics; Network approach; STEM education; Self-regulated learning; Temporal dynamicsHow do students’ perceived SRL competency, SRL behaviors, and the interactions among SRL behaviors account for their performance?Exploring.srl.processesSRLSelf-reportedTrace-otherSummativeBasic.statistical.analysisLearning.indicators2020Li, Shan, Du, Hanxiang, Xing, Wanli, Zheng, Juan, Chen, Guanhua, Xie, Charles
38Prediction of learners’ dropout in E-learning based on the unusual behaviorsCox model; E-learning; dropout prediction; survival analysis; unusual behaviorsNoneAt-risk.student.identificationSRLLms.log.dataEventSummativeOther.predictions.modelsNo.learning.focus.outcome2020Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun
38Prediction of learners’ dropout in E-learning based on the unusual behaviorsCox model; E-learning; dropout prediction; survival analysis; unusual behaviorsNoneAt-risk.student.identificationSRLLms.log.dataTrace-readingSummativeOther.predictions.modelsNo.learning.focus.outcome2020Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun
38Prediction of learners’ dropout in E-learning based on the unusual behaviorsCox model; E-learning; dropout prediction; survival analysis; unusual behaviorsNoneAt-risk.student.identificationSRLLms.log.dataTrace-quizSummativeOther.predictions.modelsNo.learning.focus.outcome2020Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun
38Prediction of learners’ dropout in E-learning based on the unusual behaviorsCox model; E-learning; dropout prediction; survival analysis; unusual behaviorsNoneAt-risk.student.identificationSRLSelf-reportedEventSummativeOther.predictions.modelsNo.learning.focus.outcome2020Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun
38Prediction of learners’ dropout in E-learning based on the unusual behaviorsCox model; E-learning; dropout prediction; survival analysis; unusual behaviorsNoneAt-risk.student.identificationSRLSelf-reportedTrace-readingSummativeOther.predictions.modelsNo.learning.focus.outcome2020Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun
38Prediction of learners’ dropout in E-learning based on the unusual behaviorsCox model; E-learning; dropout prediction; survival analysis; unusual behaviorsNoneAt-risk.student.identificationSRLSelf-reportedTrace-quizSummativeOther.predictions.modelsNo.learning.focus.outcome2020Zhou, Yizhuo, Zhao, Jin, Zhang, Jianjun
39Socio-Temporal Dynamics in Peer Interaction Eventsdigital peer Network analysiss; relational event modelling; temporalityWhat are the mechanisms of social interaction in asynchronous online discussions?Exploring.socio-dynamicssocial interactionsCustomized.log.dataEventSummativeContent.analysisTime.on.learning2020Chen, Bodong, Poquet, Oleksandra
39Socio-Temporal Dynamics in Peer Interaction Eventsdigital peer Network analysiss; relational event modelling; temporalityWhat are the mechanisms of social interaction in asynchronous online discussions?Exploring.socio-dynamicssocial interactionsCustomized.log.dataEventSummativeContent.analysisCollaboration2020Chen, Bodong, Poquet, Oleksandra
39Socio-Temporal Dynamics in Peer Interaction Eventsdigital peer Network analysiss; relational event modelling; temporalityWhat are the mechanisms of social interaction in asynchronous online discussions?Exploring.socio-dynamicssocial interactionsCustomized.log.dataEventSummativeNetwork.analysisTime.on.learning2020Chen, Bodong, Poquet, Oleksandra
39Socio-Temporal Dynamics in Peer Interaction Eventsdigital peer Network analysiss; relational event modelling; temporalityWhat are the mechanisms of social interaction in asynchronous online discussions?Exploring.socio-dynamicssocial interactionsCustomized.log.dataEventSummativeNetwork.analysisCollaboration2020Chen, Bodong, Poquet, Oleksandra
39Socio-Temporal Dynamics in Peer Interaction Eventsdigital peer Network analysiss; relational event modelling; temporalityWhat are the mechanisms of social interaction in asynchronous online discussions?Exploring.socio-dynamicssocial interactionsCustomized.log.dataEventTransitional.patternContent.analysisTime.on.learning2020Chen, Bodong, Poquet, Oleksandra
39Socio-Temporal Dynamics in Peer Interaction Eventsdigital peer Network analysiss; relational event modelling; temporalityWhat are the mechanisms of social interaction in asynchronous online discussions?Exploring.socio-dynamicssocial interactionsCustomized.log.dataEventTransitional.patternContent.analysisCollaboration2020Chen, Bodong, Poquet, Oleksandra
39Socio-Temporal Dynamics in Peer Interaction Eventsdigital peer Network analysiss; relational event modelling; temporalityWhat are the mechanisms of social interaction in asynchronous online discussions?Exploring.socio-dynamicssocial interactionsCustomized.log.dataEventTransitional.patternNetwork.analysisTime.on.learning2020Chen, Bodong, Poquet, Oleksandra
39Socio-Temporal Dynamics in Peer Interaction Eventsdigital peer Network analysiss; relational event modelling; temporalityWhat are the mechanisms of social interaction in asynchronous online discussions?Exploring.socio-dynamicssocial interactionsCustomized.log.dataEventTransitional.patternNetwork.analysisCollaboration2020Chen, Bodong, Poquet, Oleksandra
39Socio-Temporal Dynamics in Peer Interaction Eventsdigital peer Network analysiss; relational event modelling; temporalityWhat are the mechanisms of social interaction in asynchronous online discussions?Exploring.socio-dynamicssocial interactionsCustomized.log.dataTrace-forumSummativeContent.analysisTime.on.learning2020Chen, Bodong, Poquet, Oleksandra
39Socio-Temporal Dynamics in Peer Interaction Eventsdigital peer Network analysiss; relational event modelling; temporalityWhat are the mechanisms of social interaction in asynchronous online discussions?Exploring.socio-dynamicssocial interactionsCustomized.log.dataTrace-forumSummativeContent.analysisCollaboration2020Chen, Bodong, Poquet, Oleksandra
39Socio-Temporal Dynamics in Peer Interaction Eventsdigital peer Network analysiss; relational event modelling; temporalityWhat are the mechanisms of social interaction in asynchronous online discussions?Exploring.socio-dynamicssocial interactionsCustomized.log.dataTrace-forumSummativeNetwork.analysisTime.on.learning2020Chen, Bodong, Poquet, Oleksandra
39Socio-Temporal Dynamics in Peer Interaction Eventsdigital peer Network analysiss; relational event modelling; temporalityWhat are the mechanisms of social interaction in asynchronous online discussions?Exploring.socio-dynamicssocial interactionsCustomized.log.dataTrace-forumSummativeNetwork.analysisCollaboration2020Chen, Bodong, Poquet, Oleksandra
39Socio-Temporal Dynamics in Peer Interaction Eventsdigital peer Network analysiss; relational event modelling; temporalityWhat are the mechanisms of social interaction in asynchronous online discussions?Exploring.socio-dynamicssocial interactionsCustomized.log.dataTrace-forumTransitional.patternContent.analysisTime.on.learning2020Chen, Bodong, Poquet, Oleksandra
39Socio-Temporal Dynamics in Peer Interaction Eventsdigital peer Network analysiss; relational event modelling; temporalityWhat are the mechanisms of social interaction in asynchronous online discussions?Exploring.socio-dynamicssocial interactionsCustomized.log.dataTrace-forumTransitional.patternContent.analysisCollaboration2020Chen, Bodong, Poquet, Oleksandra
39Socio-Temporal Dynamics in Peer Interaction Eventsdigital peer Network analysiss; relational event modelling; temporalityWhat are the mechanisms of social interaction in asynchronous online discussions?Exploring.socio-dynamicssocial interactionsCustomized.log.dataTrace-forumTransitional.patternNetwork.analysisTime.on.learning2020Chen, Bodong, Poquet, Oleksandra
39Socio-Temporal Dynamics in Peer Interaction Eventsdigital peer Network analysiss; relational event modelling; temporalityWhat are the mechanisms of social interaction in asynchronous online discussions?Exploring.socio-dynamicssocial interactionsCustomized.log.dataTrace-forumTransitional.patternNetwork.analysisCollaboration2020Chen, Bodong, Poquet, Oleksandra
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataEventSummativeNetwork.analysisLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataEventSummativeProcess.miningLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataEventSummativeVisualization.analysisLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataEventTransitional.patternNetwork.analysisLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataEventTransitional.patternVisualization.analysisLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataTrace-readingSummativeNetwork.analysisLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataTrace-readingSummativeProcess.miningLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataTrace-readingSummativeVisualization.analysisLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataTrace-readingTransitional.patternNetwork.analysisLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataTrace-readingTransitional.patternProcess.miningLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataTrace-readingTransitional.patternVisualization.analysisLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataTrace-videoSummativeNetwork.analysisLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataTrace-videoSummativeProcess.miningLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataTrace-videoSummativeVisualization.analysisLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataTrace-videoTransitional.patternNetwork.analysisLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataTrace-videoTransitional.patternProcess.miningLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataTrace-videoTransitional.patternVisualization.analysisLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataTrace-quizSummativeNetwork.analysisLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataTrace-quizSummativeProcess.miningLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataTrace-quizSummativeVisualization.analysisLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataTrace-quizTransitional.patternNetwork.analysisLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataTrace-quizTransitional.patternProcess.miningLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
40Combining Analytic Methods to Unlock Sequential and Temporal Patterns of Self-Regulated Learningepistemic Network analysis analysis; learning analytics; micro-level processes; process mining; self-regulated learning1. To what extent can we Qualitative analysisly and quantitively characterise students’ learning behaviours from event-sequences of SRL micro-level processes, using frequency measures, Network analysis analysis, and process mining? 2. To what extent can we articulate contrasting patterns of SRL behaviours across different student groups, based on assessment performance, by using frequency measures, Network analysis analysis, and process mining? 3. To what extent can we consolidate these analytical methods to provide a coherent temporal/sequential narrative on SRL, as enacted in a blended-learning environment?Method.developmentSRLLms.log.dataTrace-quizTransitional.patternVisualization.analysisLearning.indicators2020Saint, John, Gavsevic, Dragan, Matcha, Wannisa, Uzir, Nora'Ayu Ahmad, Pardo, Abelardo
41Reinforcement Learning for the Adaptive Scheduling of Educational Activitiesadaptive learning; online education; reinforcement learningR1: How does reinforcement scheduling affect learning gains, the number of activities completed, and dropout?R2: Do early participants suffer from a worse assignment policy under reinforcement scheduling? R3: What can instructors and course designers learn from reinforcement scheduling? R4: What are the Qualitative analysis experiences of learners under reinforcement scheduling?Method.developmentNoneLms.log.dataEventSummativeOther.predictions.modelsNo.learning.focus.outcome2020Bassen, Jonathan, Balaji, Bharathan, Schaarschmidt, Michael, Thille, Candace, Painter, Jay, Zimmaro, Dawn, Games, Alex, Fast, Ethan, Mitchell, John C
41Reinforcement Learning for the Adaptive Scheduling of Educational Activitiesadaptive learning; online education; reinforcement learningR1: How does reinforcement scheduling affect learning gains, the number of activities completed, and dropout?R2: Do early participants suffer from a worse assignment policy under reinforcement scheduling? R3: What can instructors and course designers learn from reinforcement scheduling? R4: What are the Qualitative analysis experiences of learners under reinforcement scheduling?Method.developmentNonePerformance.measuresEventSummativeOther.predictions.modelsNo.learning.focus.outcome2020Bassen, Jonathan, Balaji, Bharathan, Schaarschmidt, Michael, Thille, Candace, Painter, Jay, Zimmaro, Dawn, Games, Alex, Fast, Ethan, Mitchell, John C
41Reinforcement Learning for the Adaptive Scheduling of Educational Activitiesadaptive learning; online education; reinforcement learningR1: How does reinforcement scheduling affect learning gains, the number of activities completed, and dropout?R2: Do early participants suffer from a worse assignment policy under reinforcement scheduling? R3: What can instructors and course designers learn from reinforcement scheduling? R4: What are the Qualitative analysis experiences of learners under reinforcement scheduling?At-risk.student.identificationNoneLms.log.dataEventSummativeOther.predictions.modelsNo.learning.focus.outcome2020Bassen, Jonathan, Balaji, Bharathan, Schaarschmidt, Michael, Thille, Candace, Painter, Jay, Zimmaro, Dawn, Games, Alex, Fast, Ethan, Mitchell, John C
41Reinforcement Learning for the Adaptive Scheduling of Educational Activitiesadaptive learning; online education; reinforcement learningR1: How does reinforcement scheduling affect learning gains, the number of activities completed, and dropout?R2: Do early participants suffer from a worse assignment policy under reinforcement scheduling? R3: What can instructors and course designers learn from reinforcement scheduling? R4: What are the Qualitative analysis experiences of learners under reinforcement scheduling?At-risk.student.identificationNonePerformance.measuresEventSummativeOther.predictions.modelsNo.learning.focus.outcome2020Bassen, Jonathan, Balaji, Bharathan, Schaarschmidt, Michael, Thille, Candace, Painter, Jay, Zimmaro, Dawn, Games, Alex, Fast, Ethan, Mitchell, John C
42Learners' approaches, motivation and patterns of problem-solving on lines and angles in geometry using augmented realityAugmented reality; Collaborative learning; Geometry; Immersive learning; Lines and angles; Problem-solvingRQ1: What are the perspectives of and approaches taken by the students in solving the AR learning activities when they perform it in dyads and individually? RQ2: What motivated the dyads in performing the AR learning activities as compared to the individuals?RQ3: What is the learning behavior pattern of the participating dyads while performing the AR learning activities?Non-srl.indicators.identificationcollaborative knowledge building; otherLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2020Sarkar, Pratiti, Kadam, Kapil, Pillai, Jayesh S.
42Learners' approaches, motivation and patterns of problem-solving on lines and angles in geometry using augmented realityAugmented reality; Collaborative learning; Geometry; Immersive learning; Lines and angles; Problem-solvingRQ1: What are the perspectives of and approaches taken by the students in solving the AR learning activities when they perform it in dyads and individually? RQ2: What motivated the dyads in performing the AR learning activities as compared to the individuals?RQ3: What is the learning behavior pattern of the participating dyads while performing the AR learning activities?Non-srl.indicators.identificationcollaborative knowledge building; otherLms.log.dataEventTransitional.patternVisualization.analysisLearning.indicators2020Sarkar, Pratiti, Kadam, Kapil, Pillai, Jayesh S.
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceFrequent.sequence.miningCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceCluster.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceCluster.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceVisualization.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceVisualization.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataEventGroup.event.patternFrequent.sequence.miningLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataEventGroup.event.patternFrequent.sequence.miningCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataEventGroup.event.patternCluster.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataEventGroup.event.patternCluster.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataEventGroup.event.patternVisualization.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataEventGroup.event.patternVisualization.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceFrequent.sequence.miningCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceCluster.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceCluster.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceVisualization.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceVisualization.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-readingGroup.event.patternFrequent.sequence.miningCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-readingGroup.event.patternCluster.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-readingGroup.event.patternCluster.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-readingGroup.event.patternVisualization.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-readingGroup.event.patternVisualization.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-otherEvent.sequenceFrequent.sequence.miningLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-otherEvent.sequenceFrequent.sequence.miningCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-otherEvent.sequenceCluster.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-otherEvent.sequenceCluster.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-otherEvent.sequenceVisualization.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-otherEvent.sequenceVisualization.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-otherGroup.event.patternFrequent.sequence.miningLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-otherGroup.event.patternFrequent.sequence.miningCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-otherGroup.event.patternCluster.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-otherGroup.event.patternCluster.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-otherGroup.event.patternVisualization.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLLms.log.dataTrace-otherGroup.event.patternVisualization.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedEventEvent.sequenceFrequent.sequence.miningLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedEventEvent.sequenceFrequent.sequence.miningCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedEventEvent.sequenceCluster.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedEventEvent.sequenceCluster.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedEventEvent.sequenceVisualization.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedEventEvent.sequenceVisualization.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedEventGroup.event.patternFrequent.sequence.miningLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedEventGroup.event.patternFrequent.sequence.miningCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedEventGroup.event.patternCluster.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedEventGroup.event.patternCluster.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedEventGroup.event.patternVisualization.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedEventGroup.event.patternVisualization.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-readingEvent.sequenceFrequent.sequence.miningCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-readingEvent.sequenceCluster.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-readingEvent.sequenceCluster.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-readingEvent.sequenceVisualization.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-readingEvent.sequenceVisualization.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-readingGroup.event.patternFrequent.sequence.miningCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-readingGroup.event.patternCluster.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-readingGroup.event.patternCluster.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-readingGroup.event.patternVisualization.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-readingGroup.event.patternVisualization.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-otherEvent.sequenceFrequent.sequence.miningLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-otherEvent.sequenceFrequent.sequence.miningCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-otherEvent.sequenceCluster.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-otherEvent.sequenceCluster.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-otherEvent.sequenceVisualization.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-otherEvent.sequenceVisualization.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-otherGroup.event.patternFrequent.sequence.miningLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-otherGroup.event.patternFrequent.sequence.miningCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-otherGroup.event.patternCluster.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-otherGroup.event.patternCluster.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-otherGroup.event.patternVisualization.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLSelf-reportedTrace-otherGroup.event.patternVisualization.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresEventEvent.sequenceFrequent.sequence.miningLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresEventEvent.sequenceFrequent.sequence.miningCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresEventEvent.sequenceCluster.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresEventEvent.sequenceCluster.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresEventEvent.sequenceVisualization.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresEventEvent.sequenceVisualization.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresEventGroup.event.patternFrequent.sequence.miningLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresEventGroup.event.patternFrequent.sequence.miningCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresEventGroup.event.patternCluster.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresEventGroup.event.patternCluster.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresEventGroup.event.patternVisualization.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresEventGroup.event.patternVisualization.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-readingEvent.sequenceFrequent.sequence.miningCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-readingEvent.sequenceCluster.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-readingEvent.sequenceCluster.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-readingEvent.sequenceVisualization.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-readingEvent.sequenceVisualization.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-readingGroup.event.patternFrequent.sequence.miningCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-readingGroup.event.patternCluster.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-readingGroup.event.patternCluster.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-readingGroup.event.patternVisualization.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-readingGroup.event.patternVisualization.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-otherEvent.sequenceFrequent.sequence.miningLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-otherEvent.sequenceFrequent.sequence.miningCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-otherEvent.sequenceCluster.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-otherEvent.sequenceCluster.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-otherEvent.sequenceVisualization.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-otherEvent.sequenceVisualization.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-otherGroup.event.patternFrequent.sequence.miningLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-otherGroup.event.patternFrequent.sequence.miningCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-otherGroup.event.patternCluster.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-otherGroup.event.patternCluster.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-otherGroup.event.patternVisualization.analysisLearning.indicators2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
43How Patterns of Students Dashboard Use Are Related to Their Achievement and Self-Regulatory Engagementacademic achievement; self-regulated learning; sequential pattern mining; student-facing dashboardRQ1. Are there discriminating patterns in dashboard use for students with differences in academic achievement? RQ2. Are there discriminating patterns in dashboard use for students with differences in self-regulated learning (SRL)? RQ3. Are students’ differences in achievement and SRL associated with specific patterns ofdashboard use?Exploring.srl.processesSRLPerformance.measuresTrace-otherGroup.event.patternVisualization.analysisCourse.design2020Kia, Fatemeh Salehian, Teasley, Stephanie D, Hatala, Marek, Karabenick, Stuart A, Kay, Matthew
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Exploring.srl.processesSSRL; collaborative knowledge buildingMultimodalEventSummativeBasic.statistical.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Exploring.srl.processesSSRL; collaborative knowledge buildingMultimodalEventSummativeVisualization.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Exploring.srl.processesSSRL; collaborative knowledge buildingMultimodalTrace-readingSummativeBasic.statistical.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Exploring.srl.processesSSRL; collaborative knowledge buildingMultimodalTrace-readingSummativeVisualization.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Exploring.srl.processesSSRL; collaborative knowledge buildingMultimodalTrace-quizSummativeBasic.statistical.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Exploring.srl.processesSSRL; collaborative knowledge buildingMultimodalTrace-quizSummativeVisualization.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Exploring.srl.processesSSRL; collaborative knowledge buildingSelf-reportedEventSummativeBasic.statistical.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Exploring.srl.processesSSRL; collaborative knowledge buildingSelf-reportedEventSummativeVisualization.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Exploring.srl.processesSSRL; collaborative knowledge buildingSelf-reportedTrace-readingSummativeBasic.statistical.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Exploring.srl.processesSSRL; collaborative knowledge buildingSelf-reportedTrace-readingSummativeVisualization.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Exploring.srl.processesSSRL; collaborative knowledge buildingSelf-reportedTrace-quizSummativeBasic.statistical.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Exploring.srl.processesSSRL; collaborative knowledge buildingSelf-reportedTrace-quizSummativeVisualization.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Group.comparisonSSRL; collaborative knowledge buildingMultimodalEventSummativeBasic.statistical.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Group.comparisonSSRL; collaborative knowledge buildingMultimodalEventSummativeVisualization.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Group.comparisonSSRL; collaborative knowledge buildingMultimodalTrace-readingSummativeBasic.statistical.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Group.comparisonSSRL; collaborative knowledge buildingMultimodalTrace-readingSummativeVisualization.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Group.comparisonSSRL; collaborative knowledge buildingMultimodalTrace-quizSummativeBasic.statistical.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Group.comparisonSSRL; collaborative knowledge buildingMultimodalTrace-quizSummativeVisualization.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Group.comparisonSSRL; collaborative knowledge buildingSelf-reportedEventSummativeBasic.statistical.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Group.comparisonSSRL; collaborative knowledge buildingSelf-reportedEventSummativeVisualization.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Group.comparisonSSRL; collaborative knowledge buildingSelf-reportedTrace-readingSummativeBasic.statistical.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Group.comparisonSSRL; collaborative knowledge buildingSelf-reportedTrace-readingSummativeVisualization.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Group.comparisonSSRL; collaborative knowledge buildingSelf-reportedTrace-quizSummativeBasic.statistical.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
44How does monitoring set the stage for adaptive regulation or maladaptive behavior in collaborative learning?Adaptation; Collaborative learning; Heart rate; Monitoring; Socially shared regulation of learning(RQ1) What kind of monitoring, in terms of target, valence, and phase appears during the collaboration, and to what extent do groups react to the different types of monitoring? (RQ2.1) How do maladaptive, adaptive, and on-track sequences occur during collaboration? (RQ2.2) What is the relation between adaptive and maladaptive sequences in groups’ physiological Basic statistical analysise transitions?Group.comparisonSSRL; collaborative knowledge buildingSelf-reportedTrace-quizSummativeVisualization.analysisLearning.indicators2020Sobocinski, Marta, Jarvela, Sanna, Malmberg, Jonna, Dindar, Muhterem, Isosalo, Antti, Noponen, Kai
45Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learningComputer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success?Exploring.srl.processesSRLSelf-reportedEventSummativeBasic.statistical.analysisCollaboration2020Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A.
45Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learningComputer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success?Exploring.srl.processesSRLSelf-reportedEventSummativeBasic.statistical.analysisLearning.indicators2020Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A.
45Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learningComputer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success?Exploring.srl.processesSRLPerformance.measuresEventSummativeBasic.statistical.analysisCollaboration2020Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A.
45Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learningComputer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success?Exploring.srl.processesSRLPerformance.measuresEventSummativeBasic.statistical.analysisLearning.indicators2020Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A.
45Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learningComputer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success?Exploring.srl.processesSRLMultimodalEventSummativeBasic.statistical.analysisCollaboration2020Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A.
45Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learningComputer-supported collaborative learning; Multimodal data; Physiological synchrony; Self-regulated learning1) Are there any relationships between behavioral, cognitive, motivational, and emotional regulatory processes and academic achievement? 2) Are there any relationships between the PS of students and their self-reports about behavioral, cognitive, motiva- tional, and emotional change during learning sessions? 3) Is there any relationship between the PS of students and their academic success?Exploring.srl.processesSRLMultimodalEventSummativeBasic.statistical.analysisLearning.indicators2020Dindar, Muhterem, Malmberg, Jonna, Jarvela, Sanna, Haataja, Eetu, Kirschner, Paul A.
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataEventSummativeProcess.miningLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataEventSummativeProcess.miningCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataEventSummativeBasic.statistical.analysisLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataEventSummativeBasic.statistical.analysisCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataEventTransitional.patternProcess.miningCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataEventTransitional.patternBasic.statistical.analysisLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataEventTransitional.patternBasic.statistical.analysisCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTimeSummativeProcess.miningLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTimeSummativeProcess.miningCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTimeSummativeBasic.statistical.analysisLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTimeSummativeBasic.statistical.analysisCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTimeTransitional.patternProcess.miningLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTimeTransitional.patternProcess.miningCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTimeTransitional.patternBasic.statistical.analysisLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTimeTransitional.patternBasic.statistical.analysisCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-videoSummativeProcess.miningLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-videoSummativeProcess.miningCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-videoSummativeBasic.statistical.analysisLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-videoSummativeBasic.statistical.analysisCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-videoTransitional.patternProcess.miningLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-videoTransitional.patternProcess.miningCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-videoTransitional.patternBasic.statistical.analysisLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-videoTransitional.patternBasic.statistical.analysisCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-readingSummativeProcess.miningLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-readingSummativeProcess.miningCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-readingSummativeBasic.statistical.analysisLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-readingSummativeBasic.statistical.analysisCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-readingTransitional.patternProcess.miningLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-readingTransitional.patternProcess.miningCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-readingTransitional.patternBasic.statistical.analysisLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-readingTransitional.patternBasic.statistical.analysisCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-quizSummativeProcess.miningLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-quizSummativeProcess.miningCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-quizSummativeBasic.statistical.analysisLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-quizSummativeBasic.statistical.analysisCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-quizTransitional.patternProcess.miningLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-quizTransitional.patternProcess.miningCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-quizTransitional.patternBasic.statistical.analysisLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingLms.log.dataTrace-quizTransitional.patternBasic.statistical.analysisCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualEventSummativeProcess.miningLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualEventSummativeProcess.miningCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualEventSummativeBasic.statistical.analysisLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualEventSummativeBasic.statistical.analysisCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualEventTransitional.patternProcess.miningLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualEventTransitional.patternProcess.miningCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualEventTransitional.patternBasic.statistical.analysisLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualEventTransitional.patternBasic.statistical.analysisCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTimeSummativeProcess.miningLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTimeSummativeProcess.miningCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTimeSummativeBasic.statistical.analysisLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTimeSummativeBasic.statistical.analysisCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTimeTransitional.patternProcess.miningLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTimeTransitional.patternProcess.miningCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTimeTransitional.patternBasic.statistical.analysisLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTimeTransitional.patternBasic.statistical.analysisCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-videoSummativeProcess.miningLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-videoSummativeProcess.miningCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-videoSummativeBasic.statistical.analysisLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-videoSummativeBasic.statistical.analysisCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-videoTransitional.patternProcess.miningLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-videoTransitional.patternProcess.miningCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-videoTransitional.patternBasic.statistical.analysisLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-videoTransitional.patternBasic.statistical.analysisCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-readingSummativeProcess.miningLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-readingSummativeProcess.miningCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-readingSummativeBasic.statistical.analysisLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-readingSummativeBasic.statistical.analysisCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-readingTransitional.patternProcess.miningLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-readingTransitional.patternProcess.miningCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-readingTransitional.patternBasic.statistical.analysisLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-readingTransitional.patternBasic.statistical.analysisCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-quizSummativeProcess.miningLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-quizSummativeProcess.miningCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-quizSummativeBasic.statistical.analysisLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-quizSummativeBasic.statistical.analysisCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-quizTransitional.patternProcess.miningLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-quizTransitional.patternProcess.miningCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-quizTransitional.patternBasic.statistical.analysisLearning.indicators2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
46The potential of temporal analysis: Combining log data and lag sequential analysis to investigate temporal differences between scaffolded and non-scaffolded group inquiry-based learning processesCooperative/collaborative learning; Lag sequential analysis; Postsecondary education; Scaffolding; Technology-enhanced inquiry1. How are the technological resources used in three different conditions, and what are the temporal distinctions between these conditions? 2. What kinds of IBL transition patterns do the three different conditions exhibit, and how do the IBL transition patterns differ between these conditions? 3. How do the IBL transition patterns temporally emerge in the three different conditions, and what are the temporal distinctions between these conditions?Method.developmentcollaborative knowledge buildingContextualTrace-quizTransitional.patternBasic.statistical.analysisCourse.design2020Lamsa, Joni, Hamala inen, Raija, Koskinen, Pekka, Viiri, Jouni, Mannonen, Joonas
47Rethinking Time-on-Task Estimation with Outlier Detection Accounting for Individual, Time, and Task Differenceslearning analytics; measurement; outlier detection; temporal analysis; time-on-taskThis paper investigates how outlier detection of time-on-task estimation can account for individual, time, and task differences and the resulting effect on the predictive model of academic performance.Method.developmentotherLms.log.dataEventOther.sequential.patternsOther.predictions.modelsLearning.indicators2020Nguyen, Quan
47Rethinking Time-on-Task Estimation with Outlier Detection Accounting for Individual, Time, and Task Differenceslearning analytics; measurement; outlier detection; temporal analysis; time-on-taskThis paper investigates how outlier detection of time-on-task estimation can account for individual, time, and task differences and the resulting effect on the predictive model of academic performance.Method.developmentotherLms.log.dataEventOther.sequential.patternsVisualization.analysisLearning.indicators2020Nguyen, Quan
47Rethinking Time-on-Task Estimation with Outlier Detection Accounting for Individual, Time, and Task Differenceslearning analytics; measurement; outlier detection; temporal analysis; time-on-taskThis paper investigates how outlier detection of time-on-task estimation can account for individual, time, and task differences and the resulting effect on the predictive model of academic performance.Method.developmentotherLms.log.dataTimeOther.sequential.patternsOther.predictions.modelsLearning.indicators2020Nguyen, Quan
47Rethinking Time-on-Task Estimation with Outlier Detection Accounting for Individual, Time, and Task Differenceslearning analytics; measurement; outlier detection; temporal analysis; time-on-taskThis paper investigates how outlier detection of time-on-task estimation can account for individual, time, and task differences and the resulting effect on the predictive model of academic performance.Method.developmentotherLms.log.dataTimeOther.sequential.patternsVisualization.analysisLearning.indicators2020Nguyen, Quan
48Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracingdeep learning; education; knowledge tracing; personalized learning; transformerWe show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computationsMethod.developmentknowledge tracingCustomized.log.dataEventOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2020Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
48Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracingdeep learning; education; knowledge tracing; personalized learning; transformerWe show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computationsMethod.developmentknowledge tracingCustomized.log.dataEventOther.sequential.patternsVisualization.analysisNo.learning.focus.outcome2020Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
48Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracingdeep learning; education; knowledge tracing; personalized learning; transformerWe show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computationsMethod.developmentknowledge tracingCustomized.log.dataTrace-exerciseOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2020Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
48Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracingdeep learning; education; knowledge tracing; personalized learning; transformerWe show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computationsMethod.developmentknowledge tracingCustomized.log.dataTrace-exerciseOther.sequential.patternsVisualization.analysisNo.learning.focus.outcome2020Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
48Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracingdeep learning; education; knowledge tracing; personalized learning; transformerWe show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computationsMethod.developmentknowledge tracingPerformance.measuresEventOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2020Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
48Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracingdeep learning; education; knowledge tracing; personalized learning; transformerWe show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computationsMethod.developmentknowledge tracingPerformance.measuresEventOther.sequential.patternsVisualization.analysisNo.learning.focus.outcome2020Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
48Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracingdeep learning; education; knowledge tracing; personalized learning; transformerWe show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computationsMethod.developmentknowledge tracingPerformance.measuresTrace-exerciseOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2020Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
48Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracingdeep learning; education; knowledge tracing; personalized learning; transformerWe show that SAINT effectively captures complex relations among exercises and responses using deep self-attentive computationsMethod.developmentknowledge tracingPerformance.measuresTrace-exerciseOther.sequential.patternsVisualization.analysisNo.learning.focus.outcome2020Choi, Youngduck, Lee, Youngnam, Cho, Junghyun, Baek, Jineon, Kim, Byungsoo, Cha, Yeongmin, Shin, Dongmin, Bae, Chan, Heo, Jaewe
49RKT: Relation-Aware Self-Attention for Knowledge Tracingattention Network analysiss; educational data mining; knowledge tracing; relation-aware modelwe proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the studentMethod.developmentknowledge tracingCustomized.log.dataEventOther.sequential.patternsBasic.statistical.analysisNo.learning.focus.outcome2020Pandey, Shalini, Srivastava, Jaideep
49RKT: Relation-Aware Self-Attention for Knowledge Tracingattention Network analysiss; educational data mining; knowledge tracing; relation-aware modelwe proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the studentMethod.developmentknowledge tracingCustomized.log.dataEventOther.sequential.patternsVisualization.analysisNo.learning.focus.outcome2020Pandey, Shalini, Srivastava, Jaideep
49RKT: Relation-Aware Self-Attention for Knowledge Tracingattention Network analysiss; educational data mining; knowledge tracing; relation-aware modelwe proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the studentMethod.developmentknowledge tracingCustomized.log.dataTrace-exerciseOther.sequential.patternsBasic.statistical.analysisNo.learning.focus.outcome2020Pandey, Shalini, Srivastava, Jaideep
49RKT: Relation-Aware Self-Attention for Knowledge Tracingattention Network analysiss; educational data mining; knowledge tracing; relation-aware modelwe proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the studentMethod.developmentknowledge tracingCustomized.log.dataTrace-exerciseOther.sequential.patternsVisualization.analysisNo.learning.focus.outcome2020Pandey, Shalini, Srivastava, Jaideep
49RKT: Relation-Aware Self-Attention for Knowledge Tracingattention Network analysiss; educational data mining; knowledge tracing; relation-aware modelwe proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the studentMethod.developmentknowledge tracingPerformance.measuresEventOther.sequential.patternsBasic.statistical.analysisNo.learning.focus.outcome2020Pandey, Shalini, Srivastava, Jaideep
49RKT: Relation-Aware Self-Attention for Knowledge Tracingattention Network analysiss; educational data mining; knowledge tracing; relation-aware modelwe proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the studentMethod.developmentknowledge tracingPerformance.measuresEventOther.sequential.patternsVisualization.analysisNo.learning.focus.outcome2020Pandey, Shalini, Srivastava, Jaideep
49RKT: Relation-Aware Self-Attention for Knowledge Tracingattention Network analysiss; educational data mining; knowledge tracing; relation-aware modelwe proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the studentMethod.developmentknowledge tracingPerformance.measuresTrace-exerciseOther.sequential.patternsBasic.statistical.analysisNo.learning.focus.outcome2020Pandey, Shalini, Srivastava, Jaideep
49RKT: Relation-Aware Self-Attention for Knowledge Tracingattention Network analysiss; educational data mining; knowledge tracing; relation-aware modelwe proposed a Relation-aware Self-attention mech- anism for KT task, RKT. It models a student’s interaction history and predicts her performance on the next exercise by considering contextual information obtained from its relation with the past ex- ercises and the forget behavior of the studentMethod.developmentknowledge tracingPerformance.measuresTrace-exerciseOther.sequential.patternsVisualization.analysisNo.learning.focus.outcome2020Pandey, Shalini, Srivastava, Jaideep
50Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence MattersLSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern miningRQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?Method.developmentNoneLms.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2020Malekian, Donia, Bailey, James, Kennedy, Gregor
50Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence MattersLSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern miningRQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?Method.developmentNoneLms.log.dataEventEvent.sequenceNeural.networkLearning.indicators2020Malekian, Donia, Bailey, James, Kennedy, Gregor
50Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence MattersLSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern miningRQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?Method.developmentNoneLearner.characteristicsEventEvent.sequenceFrequent.sequence.miningLearning.indicators2020Malekian, Donia, Bailey, James, Kennedy, Gregor
50Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence MattersLSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern miningRQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?Method.developmentNoneLearner.characteristicsEventEvent.sequenceNeural.networkLearning.indicators2020Malekian, Donia, Bailey, James, Kennedy, Gregor
50Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence MattersLSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern miningRQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?Method.developmentNonePerformance.measuresEventEvent.sequenceFrequent.sequence.miningLearning.indicators2020Malekian, Donia, Bailey, James, Kennedy, Gregor
50Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence MattersLSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern miningRQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?Method.developmentNonePerformance.measuresEventEvent.sequenceNeural.networkLearning.indicators2020Malekian, Donia, Bailey, James, Kennedy, Gregor
50Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence MattersLSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern miningRQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?At-risk.student.identificationNoneLms.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2020Malekian, Donia, Bailey, James, Kennedy, Gregor
50Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence MattersLSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern miningRQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?At-risk.student.identificationNoneLms.log.dataEventEvent.sequenceNeural.networkLearning.indicators2020Malekian, Donia, Bailey, James, Kennedy, Gregor
50Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence MattersLSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern miningRQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?At-risk.student.identificationNoneLearner.characteristicsEventEvent.sequenceFrequent.sequence.miningLearning.indicators2020Malekian, Donia, Bailey, James, Kennedy, Gregor
50Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence MattersLSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern miningRQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?At-risk.student.identificationNoneLearner.characteristicsEventEvent.sequenceNeural.networkLearning.indicators2020Malekian, Donia, Bailey, James, Kennedy, Gregor
50Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence MattersLSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern miningRQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?At-risk.student.identificationNonePerformance.measuresEventEvent.sequenceFrequent.sequence.miningLearning.indicators2020Malekian, Donia, Bailey, James, Kennedy, Gregor
50Prediction of Students' Assessment Readiness in Online Learning Environments: The Sequence MattersLSTM; MOOCs; assessment readiness prediction; learning analytics; sequential pattern miningRQ1. Can we develop useful prediction models for forecasting students’ readiness for assessment tasks? Does it matter if the sequential nature of students’ activities is considered in the model rather than aggregated measures? RQ2. What is the impact of considering the most recent activities prior to submission in the models compared to incorporating more historic activities applied by students? RQ3. Are there differences in assessment preparation behaviours that lead to high or low performance?At-risk.student.identificationNonePerformance.measuresEventEvent.sequenceNeural.networkLearning.indicators2020Malekian, Donia, Bailey, James, Kennedy, Gregor
51In Opinion Holders’ Shoes: Modeling Cumulative Influence for View Change in Online ArgumentationOnline discussion modeling; Persuasion; Social mediaRQ Feature Is modeling the interplay of comments beneficial (and ifso howmuch) in predicting an opinion holder’s view change? RQ Structure What representation of the sequential context helps predict view changes effectively? RQ Benefit How does it help in practice to predict view change in the context of a whole discussion?Method.developmentcollaborative knowledge buildingLearning.productEventSummativeOther.predictions.modelsCollaboration2020Guo, Zhen, Zhang, Zhe, Singh, Munindar
51In Opinion Holders’ Shoes: Modeling Cumulative Influence for View Change in Online ArgumentationOnline discussion modeling; Persuasion; Social mediaRQ Feature Is modeling the interplay of comments beneficial (and ifso howmuch) in predicting an opinion holder’s view change? RQ Structure What representation of the sequential context helps predict view changes effectively? RQ Benefit How does it help in practice to predict view change in the context of a whole discussion?Method.developmentcollaborative knowledge buildingLearning.productTrace-forumSummativeOther.predictions.modelsCollaboration2020Guo, Zhen, Zhang, Zhe, Singh, Munindar
52High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learningcollaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarilyNoneMethod.developmentcollaborative knowledge buildingLms.log.dataEventOther.sequential.patternsNetwork.analysisTime.on.learning2020Saqr, Mohammed, Nouri, Jalal
52High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learningcollaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarilyNoneMethod.developmentcollaborative knowledge buildingLms.log.dataEventSummativeNetwork.analysisTime.on.learning2020Saqr, Mohammed, Nouri, Jalal
52High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learningcollaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarilyNoneMethod.developmentcollaborative knowledge buildingLms.log.dataTrace-forumOther.sequential.patternsNetwork.analysisTime.on.learning2020Saqr, Mohammed, Nouri, Jalal
52High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learningcollaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarilyNoneMethod.developmentcollaborative knowledge buildingLms.log.dataTrace-forumSummativeNetwork.analysisTime.on.learning2020Saqr, Mohammed, Nouri, Jalal
52High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learningcollaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarilyNoneMethod.developmentcollaborative knowledge buildingLearning.productEventOther.sequential.patternsNetwork.analysisTime.on.learning2020Saqr, Mohammed, Nouri, Jalal
52High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learningcollaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarilyNoneMethod.developmentcollaborative knowledge buildingLearning.productEventSummativeNetwork.analysisTime.on.learning2020Saqr, Mohammed, Nouri, Jalal
52High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learningcollaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarilyNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-forumOther.sequential.patternsNetwork.analysisTime.on.learning2020Saqr, Mohammed, Nouri, Jalal
52High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learningcollaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarilyNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-forumSummativeNetwork.analysisTime.on.learning2020Saqr, Mohammed, Nouri, Jalal
52High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learningcollaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarilyNoneMethod.developmentcollaborative knowledge buildingPerformance.measuresEventOther.sequential.patternsNetwork.analysisTime.on.learning2020Saqr, Mohammed, Nouri, Jalal
52High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learningcollaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarilyNoneMethod.developmentcollaborative knowledge buildingPerformance.measuresEventSummativeNetwork.analysisTime.on.learning2020Saqr, Mohammed, Nouri, Jalal
52High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learningcollaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarilyNoneMethod.developmentcollaborative knowledge buildingPerformance.measuresTrace-forumOther.sequential.patternsNetwork.analysisTime.on.learning2020Saqr, Mohammed, Nouri, Jalal
52High Resolution Temporal Network Analysis to Understand and Improve Collaborative Learningcollaborative learning; learning analytics; medical education; problem-based learning; social Network analysis analysis; temporal Network analysiss; temporarilyNoneMethod.developmentcollaborative knowledge buildingPerformance.measuresTrace-forumSummativeNetwork.analysisTime.on.learning2020Saqr, Mohammed, Nouri, Jalal
53Exploring the Affordances of Sequence Mining in Educational GamesEducational games; game-based assessment; learning analytics; sequence miningTo present a proposal of sequence mining metrics: one to analyze the sequences of actions performed by students and another one to analyze their most common errors by puzzle. To present a case study with uses cases from data collected in K12 schools across the US using Shadowspect. This case study includes Visualization analysiss for teachers that exemplify how to interpret these metrics and Visualization analysis to better understand students’ behavior with the game and intervene.Method.developmentgame-based learningCustomized.log.dataEventOther.sequential.patternsVisualization.analysisLearning.indicators2020Gomez, Manuel J, Ruiperez-Valiente, Jose A, Martinez, Pedro A, Kim, Yoon Jeon
53Exploring the Affordances of Sequence Mining in Educational GamesEducational games; game-based assessment; learning analytics; sequence miningTo present a proposal of sequence mining metrics: one to analyze the sequences of actions performed by students and another one to analyze their most common errors by puzzle. To present a case study with uses cases from data collected in K12 schools across the US using Shadowspect. This case study includes Visualization analysiss for teachers that exemplify how to interpret these metrics and Visualization analysis to better understand students’ behavior with the game and intervene.Method.developmentgame-based learningCustomized.log.dataTrace-otherOther.sequential.patternsVisualization.analysisLearning.indicators2020Gomez, Manuel J, Ruiperez-Valiente, Jose A, Martinez, Pedro A, Kim, Yoon Jeon
54Analyzing Students' Behavior in Blended Learning Environment for Programming EducationBlended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering AlgorithmRQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?Non-srl.indicators.identificationfeedback engagementCustomized.log.dataEventSummativeBasic.statistical.analysisFeedback2020Luo, Jiwen, Wang, Tao
54Analyzing Students' Behavior in Blended Learning Environment for Programming EducationBlended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering AlgorithmRQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?Non-srl.indicators.identificationfeedback engagementCustomized.log.dataTrace-feedbackSummativeBasic.statistical.analysisFeedback2020Luo, Jiwen, Wang, Tao
54Analyzing Students' Behavior in Blended Learning Environment for Programming EducationBlended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering AlgorithmRQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?Non-srl.indicators.identificationfeedback engagementCustomized.log.dataTrace-videoSummativeBasic.statistical.analysisFeedback2020Luo, Jiwen, Wang, Tao
54Analyzing Students' Behavior in Blended Learning Environment for Programming EducationBlended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering AlgorithmRQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?Non-srl.indicators.identificationfeedback engagementCustomized.log.dataTrace-readingSummativeBasic.statistical.analysisFeedback2020Luo, Jiwen, Wang, Tao
54Analyzing Students' Behavior in Blended Learning Environment for Programming EducationBlended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering AlgorithmRQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?Non-srl.indicators.identificationfeedback engagementCustomized.log.dataTrace-exerciseSummativeBasic.statistical.analysisFeedback2020Luo, Jiwen, Wang, Tao
54Analyzing Students' Behavior in Blended Learning Environment for Programming EducationBlended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering AlgorithmRQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?Non-srl.indicators.identificationfeedback engagementCustomized.log.dataTrace-otherSummativeBasic.statistical.analysisFeedback2020Luo, Jiwen, Wang, Tao
54Analyzing Students' Behavior in Blended Learning Environment for Programming EducationBlended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering AlgorithmRQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?Non-srl.indicators.identificationfeedback engagementLearner.characteristicsEventSummativeBasic.statistical.analysisFeedback2020Luo, Jiwen, Wang, Tao
54Analyzing Students' Behavior in Blended Learning Environment for Programming EducationBlended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering AlgorithmRQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?Non-srl.indicators.identificationfeedback engagementLearner.characteristicsTrace-feedbackSummativeBasic.statistical.analysisFeedback2020Luo, Jiwen, Wang, Tao
54Analyzing Students' Behavior in Blended Learning Environment for Programming EducationBlended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering AlgorithmRQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?Non-srl.indicators.identificationfeedback engagementLearner.characteristicsTrace-videoSummativeBasic.statistical.analysisFeedback2020Luo, Jiwen, Wang, Tao
54Analyzing Students' Behavior in Blended Learning Environment for Programming EducationBlended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering AlgorithmRQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?Non-srl.indicators.identificationfeedback engagementLearner.characteristicsTrace-readingSummativeBasic.statistical.analysisFeedback2020Luo, Jiwen, Wang, Tao
54Analyzing Students' Behavior in Blended Learning Environment for Programming EducationBlended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering AlgorithmRQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?Non-srl.indicators.identificationfeedback engagementLearner.characteristicsTrace-exerciseSummativeBasic.statistical.analysisFeedback2020Luo, Jiwen, Wang, Tao
54Analyzing Students' Behavior in Blended Learning Environment for Programming EducationBlended Learning; Educational Data Mining; Student Category; Students Behavior Analysis; Time-series Clustering AlgorithmRQ1: How much do students interact with simple corrective feedback in a blended undergraduate physics class, and what are the patterns of engagement?RQ2: (a) What patterns of engagement with simple corrective feedback comprise productive study behaviors and are associated with stronger performance, and (b) what patterns are negatively correlated with performance?Non-srl.indicators.identificationfeedback engagementLearner.characteristicsTrace-otherSummativeBasic.statistical.analysisFeedback2020Luo, Jiwen, Wang, Tao
55The importance and meaning of session behaviour in a MOOCLearner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?Exploring.srl.processesSRLLms.log.dataEventSummativeCluster.analysisLearning.indicators2020de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55The importance and meaning of session behaviour in a MOOCLearner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?Exploring.srl.processesSRLLms.log.dataEventSummativeVisualization.analysisLearning.indicators2020de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55The importance and meaning of session behaviour in a MOOCLearner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?Exploring.srl.processesSRLLms.log.dataTimeSummativeCluster.analysisLearning.indicators2020de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55The importance and meaning of session behaviour in a MOOCLearner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?Exploring.srl.processesSRLLms.log.dataTimeSummativeVisualization.analysisLearning.indicators2020de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55The importance and meaning of session behaviour in a MOOCLearner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?Exploring.srl.processesSRLLms.log.dataTrace-videoSummativeCluster.analysisLearning.indicators2020de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55The importance and meaning of session behaviour in a MOOCLearner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?Exploring.srl.processesSRLLms.log.dataTrace-videoSummativeVisualization.analysisLearning.indicators2020de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55The importance and meaning of session behaviour in a MOOCLearner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?Exploring.srl.processesSRLLms.log.dataTrace-exerciseSummativeCluster.analysisLearning.indicators2020de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55The importance and meaning of session behaviour in a MOOCLearner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?Exploring.srl.processesSRLLms.log.dataTrace-exerciseSummativeVisualization.analysisLearning.indicators2020de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55The importance and meaning of session behaviour in a MOOCLearner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?Exploring.srl.processesSRLLms.log.dataTrace-otherSummativeCluster.analysisLearning.indicators2020de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55The importance and meaning of session behaviour in a MOOCLearner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?Exploring.srl.processesSRLLms.log.dataTrace-otherSummativeVisualization.analysisLearning.indicators2020de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55The importance and meaning of session behaviour in a MOOCLearner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?Exploring.srl.processesSRLSelf-reportedEventSummativeCluster.analysisLearning.indicators2020de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55The importance and meaning of session behaviour in a MOOCLearner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?Exploring.srl.processesSRLSelf-reportedEventSummativeVisualization.analysisLearning.indicators2020de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55The importance and meaning of session behaviour in a MOOCLearner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?Exploring.srl.processesSRLSelf-reportedTimeSummativeCluster.analysisLearning.indicators2020de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55The importance and meaning of session behaviour in a MOOCLearner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?Exploring.srl.processesSRLSelf-reportedTimeSummativeVisualization.analysisLearning.indicators2020de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55The importance and meaning of session behaviour in a MOOCLearner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?Exploring.srl.processesSRLSelf-reportedTrace-videoSummativeCluster.analysisLearning.indicators2020de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55The importance and meaning of session behaviour in a MOOCLearner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?Exploring.srl.processesSRLSelf-reportedTrace-videoSummativeVisualization.analysisLearning.indicators2020de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55The importance and meaning of session behaviour in a MOOCLearner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?Exploring.srl.processesSRLSelf-reportedTrace-exerciseSummativeCluster.analysisLearning.indicators2020de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55The importance and meaning of session behaviour in a MOOCLearner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?Exploring.srl.processesSRLSelf-reportedTrace-exerciseSummativeVisualization.analysisLearning.indicators2020de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55The importance and meaning of session behaviour in a MOOCLearner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?Exploring.srl.processesSRLSelf-reportedTrace-otherSummativeCluster.analysisLearning.indicators2020de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
55The importance and meaning of session behaviour in a MOOCLearner behaviour; Learning analytics; Massive open; Self-regulated learning (SRL); Temporal analysis; online course (MOOC)RQ1) How do learners organise their time in terms of sessions across the course? In particular, how do they distribute their time in sessions? RQ2) How do learners organise and prioritise activities (assessment actions, lecture access, discussion forum, weekly guides, and miscellaneous) within sessions across the course? RQ3) How do these patterns of session distribution and session activity relate to engagement and achievement groups (Auditors, Failed and Passed)? RQ4) How do these patterns of session distribution and session activity relate to the use of SRL skills, namely time management and effort regulation?Exploring.srl.processesSRLSelf-reportedTrace-otherSummativeVisualization.analysisLearning.indicators2020de Barba, Paula G, Malekian, Donia, Oliveira, Eduardo A, Bailey, James, Ryan, Tracii, Kennedy, Gregor
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InMethod.developmentSRLLms.log.dataEventSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InMethod.developmentSRLLms.log.dataTrace-videoSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InMethod.developmentSRLLms.log.dataTrace-exerciseSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InMethod.developmentSRLLms.log.dataTimeSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InMethod.developmentSRLSelf-reportedEventSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InMethod.developmentSRLSelf-reportedTrace-videoSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InMethod.developmentSRLSelf-reportedTrace-exerciseSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InMethod.developmentSRLSelf-reportedTimeSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InAt-risk.student.identificationSRLLms.log.dataEventSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InAt-risk.student.identificationSRLLms.log.dataTrace-videoSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InAt-risk.student.identificationSRLLms.log.dataTrace-exerciseSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InAt-risk.student.identificationSRLLms.log.dataTimeSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InAt-risk.student.identificationSRLSelf-reportedEventSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InAt-risk.student.identificationSRLSelf-reportedTrace-videoSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InAt-risk.student.identificationSRLSelf-reportedTrace-exerciseSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InAt-risk.student.identificationSRLSelf-reportedTimeSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InExploring.srl.processesSRLLms.log.dataEventSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InExploring.srl.processesSRLLms.log.dataTrace-videoSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InExploring.srl.processesSRLLms.log.dataTrace-exerciseSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InExploring.srl.processesSRLLms.log.dataTimeSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InExploring.srl.processesSRLSelf-reportedEventSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InExploring.srl.processesSRLSelf-reportedTrace-videoSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InExploring.srl.processesSRLSelf-reportedTrace-exerciseSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
56Temporal analysis for dropout prediction using self-regulated learning strategies in self-paced MOOCsData science applications in education; Distance education and online learning; Lifelong learning; Post-secondary educationRQ1: What is the predictive power of self-reported SRL strategies in dropout prediction? RQ2: What is the predictive power of event-based SRL strategies in dropout prediction? RQ3: When is the best moment to predict dropout in a self-paced MOOC? InExploring.srl.processesSRLSelf-reportedTimeSummativeOther.predictions.modelsNo.learning.focus.outcome2020Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Maldonado-Mahauad, Jorge, Perez-Sanagustin, Mar, Alario-Hoyos, Carlos, {Delgado Kloos}, Carlos
57Temporal analysis of multimodal data to predict collaborative learning outcomescollaborative learning; learning analytics; multimodal(RQ1) What relations between learning outcomes and collaborative process variables can be exposed through temporal analysis that are not visible in overall frequency analyses? To answer this question, we compare the analysis of different single data streams analyzed as averages and counts to their use in an LSTM. (RQ2) Does multimodal data provide more accurate predictions from those gained by unimodal data for collaborative learning outcomes? To address this research question, we compared the results from the data streams used individually in an LSTM to combinations of the variables. (RQ3) Are there combinations of multimodal data that may be more predictive than others?Method.developmentcollaborative knowledge buildingMultimodalEventNoneOther.predictions.modelsNo.learning.focus.outcome2020Olsen, Jennifer K, Sharma, Kshitij, Rummel, Nikol, Aleven, Vincent
57Temporal analysis of multimodal data to predict collaborative learning outcomescollaborative learning; learning analytics; multimodal(RQ1) What relations between learning outcomes and collaborative process variables can be exposed through temporal analysis that are not visible in overall frequency analyses? To answer this question, we compare the analysis of different single data streams analyzed as averages and counts to their use in an LSTM. (RQ2) Does multimodal data provide more accurate predictions from those gained by unimodal data for collaborative learning outcomes? To address this research question, we compared the results from the data streams used individually in an LSTM to combinations of the variables. (RQ3) Are there combinations of multimodal data that may be more predictive than others?Method.developmentcollaborative knowledge buildingMultimodalTrace-otherNoneOther.predictions.modelsNo.learning.focus.outcome2020Olsen, Jennifer K, Sharma, Kshitij, Rummel, Nikol, Aleven, Vincent
58Towards Understanding the Lifespan and Spread of Ideas: Epidemiological Modeling of Participation on Twitterconnectivism; engagement patterns; epidemiology; ideas; knowledge creation; Network analysised learningIn this paper, we present preliminary work of tackling this challenge by applying epidemiological modeling to the evolution of ideas.Method.developmentcollaborative knowledge buildingLearning.productEventSummativeBasic.statistical.analysisNo.learning.focus.outcome2020Peri, Sai Santosh Sasank, Chen, Bodong, Dougall, Angela Liegey, Siemens, George
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherLms.log.dataEventGroup.event.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherLms.log.dataEventGroup.event.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherLms.log.dataEventGroup.event.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherLms.log.dataEventTransitional.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherLms.log.dataEventTransitional.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherLms.log.dataEventTransitional.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherLms.log.dataTimeGroup.event.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherLms.log.dataTimeGroup.event.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherLms.log.dataTimeGroup.event.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherLms.log.dataTimeTransitional.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherLms.log.dataTimeTransitional.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherLms.log.dataTimeTransitional.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherLms.log.dataTrace-quizGroup.event.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherLms.log.dataTrace-quizGroup.event.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherLms.log.dataTrace-quizGroup.event.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherLms.log.dataTrace-quizTransitional.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherLms.log.dataTrace-quizTransitional.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherLms.log.dataTrace-quizTransitional.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherPerformance.measuresEventGroup.event.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherPerformance.measuresEventGroup.event.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherPerformance.measuresEventGroup.event.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherPerformance.measuresEventTransitional.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherPerformance.measuresEventTransitional.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherPerformance.measuresEventTransitional.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherPerformance.measuresTimeGroup.event.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherPerformance.measuresTimeGroup.event.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherPerformance.measuresTimeGroup.event.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherPerformance.measuresTimeTransitional.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherPerformance.measuresTimeTransitional.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherPerformance.measuresTimeTransitional.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherPerformance.measuresTrace-quizGroup.event.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherPerformance.measuresTrace-quizGroup.event.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherPerformance.measuresTrace-quizGroup.event.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherPerformance.measuresTrace-quizTransitional.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherPerformance.measuresTrace-quizTransitional.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Method.developmentotherPerformance.measuresTrace-quizTransitional.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherLms.log.dataEventGroup.event.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherLms.log.dataEventGroup.event.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherLms.log.dataEventGroup.event.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherLms.log.dataEventTransitional.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherLms.log.dataEventTransitional.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherLms.log.dataEventTransitional.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherLms.log.dataTimeGroup.event.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherLms.log.dataTimeGroup.event.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherLms.log.dataTimeGroup.event.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherLms.log.dataTimeTransitional.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherLms.log.dataTimeTransitional.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherLms.log.dataTimeTransitional.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherLms.log.dataTrace-quizGroup.event.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherLms.log.dataTrace-quizGroup.event.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherLms.log.dataTrace-quizGroup.event.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherLms.log.dataTrace-quizTransitional.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherLms.log.dataTrace-quizTransitional.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherLms.log.dataTrace-quizTransitional.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherPerformance.measuresEventGroup.event.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherPerformance.measuresEventGroup.event.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherPerformance.measuresEventGroup.event.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherPerformance.measuresEventTransitional.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherPerformance.measuresEventTransitional.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherPerformance.measuresEventTransitional.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherPerformance.measuresTimeGroup.event.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherPerformance.measuresTimeGroup.event.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherPerformance.measuresTimeGroup.event.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherPerformance.measuresTimeTransitional.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherPerformance.measuresTimeTransitional.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherPerformance.measuresTimeTransitional.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherPerformance.measuresTrace-quizGroup.event.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherPerformance.measuresTrace-quizGroup.event.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherPerformance.measuresTrace-quizGroup.event.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherPerformance.measuresTrace-quizTransitional.patternCluster.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherPerformance.measuresTrace-quizTransitional.patternProcess.miningTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
59Exploring Student Approaches to Learning through Sequence Analysis of Reading Logsassociation rule mining; Cluster analysis ; learning analytics; reading logs; sequence analysis; study approachesRQ 1: Is it possible to identify surface, deep and strategic learners from the reading logs? RQ 2: What is the relationship between study approaches and learning outcomes? RQ 3: What are the characteristic association rules between surface, deep, strategic learners’ reading behaviors and their academic performance?Group.comparisonotherPerformance.measuresTrace-quizTransitional.patternVisualization.analysisTime.on.learning2020Ak{cc}apinar, G{\"o}khan, Chen, Mei-Rong Alice, Majumdar, Rwitajit, Flanagan, Brendan, Ogata, Hiroaki
60CLMS-Net: Dropout Prediction in MOOCs with Deep Learning (2019)MOOCs; deep learning; dropout prediction; learning analyticsNoneMethod.developmentNoneLms.log.dataEventOther.sequential.patternsNeural.networkNo.learning.focus.outcome2019Wu, Nannan, Zhang, Lei, Gao, Yi, Zhang, Mingfei, Sun, Xia, Feng, Jun
60CLMS-Net: Dropout Prediction in MOOCs with Deep Learning (2019)MOOCs; deep learning; dropout prediction; learning analyticsNoneMethod.developmentNoneLms.log.dataTimeOther.sequential.patternsNeural.networkNo.learning.focus.outcome2019Wu, Nannan, Zhang, Lei, Gao, Yi, Zhang, Mingfei, Sun, Xia, Feng, Jun
61Characteristics of Visual Attention for the Assessment of Conceptual Change: An Eye-Tracking Studyassessment; conceptual change; eye-tracking; visual attentionH1. The CCG spend more time on areas related to scientific conceptions while the NCCG spend more time on areas related to misconceptions.H2. The characteristics of fixation transactions among AOIs are different between the CCG and the NCCG.Non-srl.indicators.identificationotherMultimodalEventTransitional.patternProcess.miningNo.learning.focus.outcome2019Jin, Laipeng, Yu, Dongchuan
61Characteristics of Visual Attention for the Assessment of Conceptual Change: An Eye-Tracking Studyassessment; conceptual change; eye-tracking; visual attentionH1. The CCG spend more time on areas related to scientific conceptions while the NCCG spend more time on areas related to misconceptions.H2. The characteristics of fixation transactions among AOIs are different between the CCG and the NCCG.Non-srl.indicators.identificationotherMultimodalEventTransitional.patternVisualization.analysisNo.learning.focus.outcome2019Jin, Laipeng, Yu, Dongchuan
62An Analysis of Student Representation, Representative Features and Classification Algorithms to Predict Degree DropoutDegree Dropout Analysis; Dropout Prediction; Features Extraction; Student Representation; Temporal AnalysisNoneMethod.developmentNoneLms.log.dataEventNoneOther.predictions.modelsNo.learning.focus.outcome2019Manrique, Ruben, Nunes, Bernardo Pereira, Marino, Olga, Casanova, Marco Antonio, Nurmikko-Fuller, Terhi
62An Analysis of Student Representation, Representative Features and Classification Algorithms to Predict Degree DropoutDegree Dropout Analysis; Dropout Prediction; Features Extraction; Student Representation; Temporal AnalysisNoneMethod.developmentNonePerformance.measuresEventNoneOther.predictions.modelsNo.learning.focus.outcome2019Manrique, Ruben, Nunes, Bernardo Pereira, Marino, Olga, Casanova, Marco Antonio, Nurmikko-Fuller, Terhi
62An Analysis of Student Representation, Representative Features and Classification Algorithms to Predict Degree DropoutDegree Dropout Analysis; Dropout Prediction; Features Extraction; Student Representation; Temporal AnalysisNoneAt-risk.student.identificationNoneLms.log.dataEventNoneOther.predictions.modelsNo.learning.focus.outcome2019Manrique, Ruben, Nunes, Bernardo Pereira, Marino, Olga, Casanova, Marco Antonio, Nurmikko-Fuller, Terhi
62An Analysis of Student Representation, Representative Features and Classification Algorithms to Predict Degree DropoutDegree Dropout Analysis; Dropout Prediction; Features Extraction; Student Representation; Temporal AnalysisNoneAt-risk.student.identificationNonePerformance.measuresEventNoneOther.predictions.modelsNo.learning.focus.outcome2019Manrique, Ruben, Nunes, Bernardo Pereira, Marino, Olga, Casanova, Marco Antonio, Nurmikko-Fuller, Terhi
63Predicting Dynamic Embedding Trajectory in Temporal Interaction Networksdeep learning; embeddingsNoneMethod.developmentNoneLms.log.dataEventSummativeOther.predictions.modelsNo.learning.focus.outcome2019Kumar, Srijan, Zhang, Xikun, Leskovec, Jure
63Predicting Dynamic Embedding Trajectory in Temporal Interaction Networksdeep learning; embeddingsNoneAt-risk.student.identificationNoneLms.log.dataEventSummativeOther.predictions.modelsNo.learning.focus.outcome2019Kumar, Srijan, Zhang, Xikun, Leskovec, Jure
64Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive loadcognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace dataO1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.Non-srl.indicators.identificationotherLms.log.dataEventSummativeBasic.statistical.analysisLearning.indicators2019Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive loadcognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace dataO1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.Non-srl.indicators.identificationotherLms.log.dataTrace-exerciseSummativeBasic.statistical.analysisLearning.indicators2019Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive loadcognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace dataO1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.Non-srl.indicators.identificationotherLms.log.dataTrace-videoSummativeBasic.statistical.analysisLearning.indicators2019Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive loadcognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace dataO1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.Non-srl.indicators.identificationotherLms.log.dataTrace-readingSummativeBasic.statistical.analysisLearning.indicators2019Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive loadcognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace dataO1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.Non-srl.indicators.identificationotherLms.log.dataTrace-quizSummativeBasic.statistical.analysisLearning.indicators2019Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive loadcognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace dataO1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.Non-srl.indicators.identificationotherLms.log.dataTimeSummativeBasic.statistical.analysisLearning.indicators2019Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive loadcognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace dataO1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.Non-srl.indicators.identificationotherPerformance.measuresEventSummativeBasic.statistical.analysisLearning.indicators2019Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive loadcognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace dataO1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.Non-srl.indicators.identificationotherPerformance.measuresTrace-exerciseSummativeBasic.statistical.analysisLearning.indicators2019Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive loadcognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace dataO1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.Non-srl.indicators.identificationotherPerformance.measuresTrace-videoSummativeBasic.statistical.analysisLearning.indicators2019Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive loadcognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace dataO1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.Non-srl.indicators.identificationotherPerformance.measuresTrace-readingSummativeBasic.statistical.analysisLearning.indicators2019Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive loadcognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace dataO1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.Non-srl.indicators.identificationotherPerformance.measuresTrace-quizSummativeBasic.statistical.analysisLearning.indicators2019Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive loadcognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace dataO1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.Non-srl.indicators.identificationotherPerformance.measuresTimeSummativeBasic.statistical.analysisLearning.indicators2019Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive loadcognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace dataO1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.Non-srl.indicators.identificationotherSelf-reportedEventSummativeBasic.statistical.analysisLearning.indicators2019Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive loadcognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace dataO1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.Non-srl.indicators.identificationotherSelf-reportedTrace-exerciseSummativeBasic.statistical.analysisLearning.indicators2019Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive loadcognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace dataO1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.Non-srl.indicators.identificationotherSelf-reportedTrace-videoSummativeBasic.statistical.analysisLearning.indicators2019Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive loadcognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace dataO1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.Non-srl.indicators.identificationotherSelf-reportedTrace-readingSummativeBasic.statistical.analysisLearning.indicators2019Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive loadcognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace dataO1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.Non-srl.indicators.identificationotherSelf-reportedTrace-quizSummativeBasic.statistical.analysisLearning.indicators2019Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
64Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive loadcognitive load; learning analytics; perceived difficulty; self-efficacy; self-reports; trace dataO1. Explore how students interacted with the activity evaluation tool. Since this was an exploratory study, we first wanted to get a basic insight into the students’ interaction with this tool (e.g., how much they used it, when they used it, what were their dominant perceptions) O2. Examine and quantify the association between the students’ engagement with the learning activities - including frequency, timeliness, and outcome (in case of formative assessment) of interaction - and their perception of the difficulty and self-ef- ficacy effect of those activities. O3. Examine the presence / level of association between the stu- dents’ course performance and their perception of learning ac- tivities in terms of the difficulty and effect on the students’ self- efficacy for the corresponding course units.Non-srl.indicators.identificationotherSelf-reportedTimeSummativeBasic.statistical.analysisLearning.indicators2019Jovanovic, Jelena, Gavsevic, Dragan, Pardo, Abelardo, Dawson, Shane, Whitelock-Wainwright, Alexander
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceProcess.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceVisualization.analysisLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Exploring.srl.processesSRLLms.log.dataEventTransitional.patternFrequent.sequence.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Exploring.srl.processesSRLLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Exploring.srl.processesSRLLms.log.dataEventTransitional.patternVisualization.analysisLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Exploring.srl.processesSRLLms.log.dataTrace-videoEvent.sequenceFrequent.sequence.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Exploring.srl.processesSRLLms.log.dataTrace-videoEvent.sequenceProcess.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Exploring.srl.processesSRLLms.log.dataTrace-videoEvent.sequenceVisualization.analysisLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Exploring.srl.processesSRLLms.log.dataTrace-videoTransitional.patternFrequent.sequence.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Exploring.srl.processesSRLLms.log.dataTrace-videoTransitional.patternProcess.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Exploring.srl.processesSRLLms.log.dataTrace-videoTransitional.patternVisualization.analysisLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Exploring.srl.processesSRLLms.log.dataTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Exploring.srl.processesSRLLms.log.dataTrace-quizEvent.sequenceProcess.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Exploring.srl.processesSRLLms.log.dataTrace-quizEvent.sequenceVisualization.analysisLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Exploring.srl.processesSRLLms.log.dataTrace-quizTransitional.patternFrequent.sequence.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Exploring.srl.processesSRLLms.log.dataTrace-quizTransitional.patternProcess.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Exploring.srl.processesSRLLms.log.dataTrace-quizTransitional.patternVisualization.analysisLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Group.comparisonSRLLms.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Group.comparisonSRLLms.log.dataEventEvent.sequenceProcess.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Group.comparisonSRLLms.log.dataEventEvent.sequenceVisualization.analysisLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Group.comparisonSRLLms.log.dataEventTransitional.patternFrequent.sequence.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Group.comparisonSRLLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Group.comparisonSRLLms.log.dataEventTransitional.patternVisualization.analysisLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Group.comparisonSRLLms.log.dataTrace-videoEvent.sequenceFrequent.sequence.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Group.comparisonSRLLms.log.dataTrace-videoEvent.sequenceProcess.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Group.comparisonSRLLms.log.dataTrace-videoEvent.sequenceVisualization.analysisLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Group.comparisonSRLLms.log.dataTrace-videoTransitional.patternFrequent.sequence.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Group.comparisonSRLLms.log.dataTrace-videoTransitional.patternProcess.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Group.comparisonSRLLms.log.dataTrace-videoTransitional.patternVisualization.analysisLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Group.comparisonSRLLms.log.dataTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Group.comparisonSRLLms.log.dataTrace-quizEvent.sequenceProcess.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Group.comparisonSRLLms.log.dataTrace-quizEvent.sequenceVisualization.analysisLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Group.comparisonSRLLms.log.dataTrace-quizTransitional.patternFrequent.sequence.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Group.comparisonSRLLms.log.dataTrace-quizTransitional.patternProcess.miningLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
65Exploring sequences of learner activities in relation to self-regulated learning in a massive open online courseClickstream data; Learning analytics; Massive open online course (MOOC); Self-regulated learning (SRL); Sequential pattern miningWhat are the differences in sequences of learner activities between SRL-prompt viewers and non-viewers in a MOOC embedded with SRL-prompt videos?Group.comparisonSRLLms.log.dataTrace-quizTransitional.patternVisualization.analysisLearning.indicators2019Wong, Jacqueline, Khalil, Mohammad, Baars, Martine, de Koning, Bj{\"o}rn B, Paas, Fred
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventEvent.sequenceFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventEvent.sequenceProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventEvent.sequenceProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventEvent.sequenceCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventEvent.sequenceVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventEvent.sequenceVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventGroup.event.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventGroup.event.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventGroup.event.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventGroup.event.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventGroup.event.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventGroup.event.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventTransitional.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventTransitional.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventTransitional.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventTransitional.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventTransitional.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventTransitional.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataEventTransitional.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackEvent.sequenceFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackEvent.sequenceProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackEvent.sequenceProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackEvent.sequenceCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackEvent.sequenceVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackEvent.sequenceVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackGroup.event.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackGroup.event.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackGroup.event.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackGroup.event.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackGroup.event.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackGroup.event.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackTransitional.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackTransitional.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackTransitional.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackTransitional.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackTransitional.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackTransitional.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackTransitional.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-feedbackTransitional.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingEvent.sequenceFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingEvent.sequenceProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingEvent.sequenceProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingEvent.sequenceCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingEvent.sequenceVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingEvent.sequenceVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingGroup.event.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingGroup.event.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingGroup.event.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingGroup.event.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingGroup.event.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingGroup.event.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingTransitional.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingTransitional.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingTransitional.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingTransitional.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingTransitional.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingTransitional.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingTransitional.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-readingTransitional.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoEvent.sequenceFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoEvent.sequenceProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoEvent.sequenceProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoEvent.sequenceCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoEvent.sequenceVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoEvent.sequenceVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoGroup.event.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoGroup.event.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoGroup.event.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoGroup.event.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoGroup.event.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoGroup.event.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoTransitional.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoTransitional.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoTransitional.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoTransitional.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoTransitional.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoTransitional.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoTransitional.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-videoTransitional.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseEvent.sequenceFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseEvent.sequenceProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseEvent.sequenceProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseEvent.sequenceCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseEvent.sequenceVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseEvent.sequenceVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseGroup.event.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseGroup.event.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseGroup.event.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseGroup.event.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseGroup.event.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseGroup.event.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseTransitional.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseTransitional.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseTransitional.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseTransitional.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseTransitional.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseTransitional.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseTransitional.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLLms.log.dataTrace-exerciseTransitional.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventEvent.sequenceFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventEvent.sequenceProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventEvent.sequenceProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventEvent.sequenceCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventEvent.sequenceVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventEvent.sequenceVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventGroup.event.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventGroup.event.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventGroup.event.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventGroup.event.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventGroup.event.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventGroup.event.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventTransitional.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventTransitional.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventTransitional.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventTransitional.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventTransitional.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventTransitional.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventTransitional.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresEventTransitional.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackEvent.sequenceFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackEvent.sequenceProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackEvent.sequenceProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackEvent.sequenceCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackEvent.sequenceVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackEvent.sequenceVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackGroup.event.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackGroup.event.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackGroup.event.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackGroup.event.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackGroup.event.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackGroup.event.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackTransitional.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackTransitional.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackTransitional.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackTransitional.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackTransitional.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackTransitional.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackTransitional.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-feedbackTransitional.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingEvent.sequenceFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingEvent.sequenceProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingEvent.sequenceProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingEvent.sequenceCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingEvent.sequenceVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingEvent.sequenceVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingGroup.event.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingGroup.event.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingGroup.event.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingGroup.event.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingGroup.event.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingGroup.event.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingTransitional.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingTransitional.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingTransitional.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingTransitional.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingTransitional.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingTransitional.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingTransitional.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-readingTransitional.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoEvent.sequenceFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoEvent.sequenceProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoEvent.sequenceProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoEvent.sequenceCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoEvent.sequenceVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoEvent.sequenceVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoGroup.event.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoGroup.event.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoGroup.event.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoGroup.event.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoGroup.event.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoGroup.event.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoTransitional.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoTransitional.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoTransitional.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoTransitional.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoTransitional.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoTransitional.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoTransitional.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-videoTransitional.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseEvent.sequenceFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseEvent.sequenceProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseEvent.sequenceProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseEvent.sequenceCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseEvent.sequenceVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseEvent.sequenceVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseGroup.event.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseGroup.event.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseGroup.event.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseGroup.event.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseGroup.event.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseGroup.event.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseTransitional.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseTransitional.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseTransitional.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseTransitional.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseTransitional.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseTransitional.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseTransitional.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Method.developmentSRLPerformance.measuresTrace-exerciseTransitional.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventGroup.event.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventGroup.event.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventGroup.event.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventGroup.event.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventGroup.event.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventGroup.event.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventTransitional.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventTransitional.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventTransitional.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventTransitional.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventTransitional.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventTransitional.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataEventTransitional.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackEvent.sequenceFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackEvent.sequenceProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackEvent.sequenceProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackEvent.sequenceCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackEvent.sequenceVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackEvent.sequenceVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackGroup.event.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackGroup.event.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackGroup.event.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackGroup.event.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackGroup.event.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackGroup.event.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackTransitional.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackTransitional.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackTransitional.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackTransitional.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackTransitional.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackTransitional.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackTransitional.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-feedbackTransitional.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingGroup.event.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingGroup.event.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingGroup.event.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingGroup.event.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingGroup.event.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingGroup.event.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingTransitional.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingTransitional.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingTransitional.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingTransitional.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingTransitional.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingTransitional.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingTransitional.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-readingTransitional.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoEvent.sequenceFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoEvent.sequenceProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoEvent.sequenceProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoEvent.sequenceCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoEvent.sequenceVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoEvent.sequenceVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoGroup.event.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoGroup.event.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoGroup.event.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoGroup.event.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoGroup.event.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoGroup.event.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoTransitional.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoTransitional.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoTransitional.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoTransitional.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoTransitional.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoTransitional.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoTransitional.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-videoTransitional.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseEvent.sequenceFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseEvent.sequenceProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseEvent.sequenceProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseEvent.sequenceCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseEvent.sequenceVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseEvent.sequenceVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseGroup.event.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseGroup.event.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseGroup.event.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseGroup.event.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseGroup.event.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseGroup.event.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseTransitional.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseTransitional.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseTransitional.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseTransitional.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseTransitional.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseTransitional.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseTransitional.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLLms.log.dataTrace-exerciseTransitional.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventEvent.sequenceFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventEvent.sequenceProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventEvent.sequenceProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventEvent.sequenceCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventEvent.sequenceVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventEvent.sequenceVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventGroup.event.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventGroup.event.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventGroup.event.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventGroup.event.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventGroup.event.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventGroup.event.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventTransitional.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventTransitional.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventTransitional.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventTransitional.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventTransitional.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventTransitional.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventTransitional.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresEventTransitional.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackEvent.sequenceFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackEvent.sequenceProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackEvent.sequenceProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackEvent.sequenceCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackEvent.sequenceVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackEvent.sequenceVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackGroup.event.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackGroup.event.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackGroup.event.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackGroup.event.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackGroup.event.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackGroup.event.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackTransitional.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackTransitional.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackTransitional.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackTransitional.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackTransitional.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackTransitional.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackTransitional.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-feedbackTransitional.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingEvent.sequenceFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingEvent.sequenceProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingEvent.sequenceProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingEvent.sequenceCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingEvent.sequenceVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingEvent.sequenceVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingGroup.event.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingGroup.event.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingGroup.event.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingGroup.event.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingGroup.event.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingGroup.event.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingTransitional.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingTransitional.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingTransitional.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingTransitional.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingTransitional.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingTransitional.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingTransitional.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-readingTransitional.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoEvent.sequenceFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoEvent.sequenceProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoEvent.sequenceProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoEvent.sequenceCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoEvent.sequenceVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoEvent.sequenceVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoGroup.event.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoGroup.event.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoGroup.event.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoGroup.event.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoGroup.event.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoGroup.event.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoTransitional.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoTransitional.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoTransitional.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoTransitional.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoTransitional.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoTransitional.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoTransitional.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-videoTransitional.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseEvent.sequenceFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseEvent.sequenceProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseEvent.sequenceProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseEvent.sequenceCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseEvent.sequenceVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseEvent.sequenceVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseGroup.event.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseGroup.event.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseGroup.event.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseGroup.event.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseGroup.event.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseGroup.event.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseTransitional.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseTransitional.patternFrequent.sequence.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseTransitional.patternProcess.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseTransitional.patternProcess.miningFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseTransitional.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseTransitional.patternCluster.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseTransitional.patternVisualization.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
66Analytics of Learning Strategies: Associations with Academic Performance and FeedbackData Mining; Feedback; Learning Analytics; Learning Strategies; Learning Tactics; Self-regulated LearningGiven a sequence of actions across several time frames, can we detect theoretically meaningful learning tactics and strategies applied by students when preparing for face-to-face sessions in a flipped classroom?Is there an association between the identified learning strat- egies and the students’ academic performance in a flipped class- room?Are feedback interventions associated with the students' choice of learning strategies and their performance in a flipped classroom?Exploring.srl.processesSRLPerformance.measuresTrace-exerciseTransitional.patternVisualization.analysisFeedback2019Matcha, Wannisa, Gavsevic, Dragan, Uzir, Nora'Ayu Ahmad, Jovanovic, Jelena, Pardo, Abelardo
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataEventEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataEventGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataEventGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-videoEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-videoEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-videoGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-videoGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-quizEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-quizGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-quizGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-readingEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-readingGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-quizEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-quizGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-quizGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-readingEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-readingGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-forumEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-forumEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-forumGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLLms.log.dataTrace-forumGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresEventEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresEventEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresEventGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresEventGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-videoEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-videoEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-videoGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-videoGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-quizEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-quizGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-quizGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-readingEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-readingGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-quizEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-quizGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-quizGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-readingEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-readingGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-forumEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-forumEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-forumGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Method.developmentSRLPerformance.measuresTrace-forumGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataEventEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataEventGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataEventGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-videoEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-videoEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-videoGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-videoGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-quizEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-quizGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-quizGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-readingGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-quizEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-quizGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-quizGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-readingEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-readingGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-forumEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-forumEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-forumGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLLms.log.dataTrace-forumGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresEventEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresEventEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresEventGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresEventGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-videoEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-videoEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-videoGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-videoGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-quizEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-quizGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-quizGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-readingEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-readingGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-quizEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-quizGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-quizGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-readingEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-readingGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-forumEvent.sequenceFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-forumEvent.sequenceCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-forumGroup.event.patternFrequent.sequence.miningLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
67Analytics of Learning Strategies: The Association with the Personality Traitsapproaches to learning; learning analytics; learning strategies; personality traitsRQ1: Given a MOOC, can we detect theoretically meaningful learning tactics and strategies? Ifso, is there any association ofthe detected strategies and academic performance?RQ2: Is there an association between the learning strategies adopted by learners in a MOOC and any oftheir personality traits?Exploring.srl.processesSRLPerformance.measuresTrace-forumGroup.event.patternCluster.analysisLearning.indicators2019Matcha, Wannisa, Gavsevic, Dragan, Jovanovic, Jelena, Uzir, Nora'ayu Ahmad, Oliver, Chris W, Murray, Andrew, Gasevic, Danijela
68An application framework for mining online learning processes through event-logsMoodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic minerRQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be?Method.developmentotherLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2019Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
68An application framework for mining online learning processes through event-logsMoodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic minerRQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be?Method.developmentotherLms.log.dataEventTransitional.patternVisualization.analysisLearning.indicators2019Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
68An application framework for mining online learning processes through event-logsMoodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic minerRQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be?Method.developmentotherLms.log.dataTrace-readingTransitional.patternProcess.miningLearning.indicators2019Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
68An application framework for mining online learning processes through event-logsMoodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic minerRQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be?Method.developmentotherLms.log.dataTrace-readingTransitional.patternVisualization.analysisLearning.indicators2019Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
68An application framework for mining online learning processes through event-logsMoodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic minerRQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be?Method.developmentotherLms.log.dataTrace-forumTransitional.patternProcess.miningLearning.indicators2019Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
68An application framework for mining online learning processes through event-logsMoodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic minerRQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be?Method.developmentotherLms.log.dataTrace-forumTransitional.patternVisualization.analysisLearning.indicators2019Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
68An application framework for mining online learning processes through event-logsMoodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic minerRQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be?Method.developmentotherLms.log.dataTrace-otherTransitional.patternProcess.miningLearning.indicators2019Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
68An application framework for mining online learning processes through event-logsMoodle, Latent class analysis, Process.mining, Educational process mining, Fuzzy miner, Heuristic minerRQ1. How can activity-based Cluster analysis be performed based on the efforts of students? RQ2. How can sequential activity structures be discovered? RQ3. How can the general process flow that students follow be monitored?RQ4. How can the Network analysis formed by students entering the system from the same channel be?Method.developmentotherLms.log.dataTrace-otherTransitional.patternVisualization.analysisLearning.indicators2019Ozdagoglu, Guzin, Oztas, Gulin Zeynep, Cagliyangil, Mehmet
69Social Network analysising and academic performance: A longitudinal perspectiveAcademic performance; Bidirectional association; Longitudinal study; Social Network analysising sites; Temporal perspectiveto better understand the temporal association between SNS use and academic performance.Method.developmentotherPerformance.measuresEventSummativeBasic.statistical.analysisNo.learning.focus.outcome2019Doleck, Tenzin, Lajoie, Susanne P., Bazelais, Paul
69Social Network analysising and academic performance: A longitudinal perspectiveAcademic performance; Bidirectional association; Longitudinal study; Social Network analysising sites; Temporal perspectiveto better understand the temporal association between SNS use and academic performance.Method.developmentotherPerformance.measuresTimeSummativeBasic.statistical.analysisNo.learning.focus.outcome2019Doleck, Tenzin, Lajoie, Susanne P., Bazelais, Paul
69Social Network analysising and academic performance: A longitudinal perspectiveAcademic performance; Bidirectional association; Longitudinal study; Social Network analysising sites; Temporal perspectiveto better understand the temporal association between SNS use and academic performance.Method.developmentotherSelf-reportedEventSummativeBasic.statistical.analysisNo.learning.focus.outcome2019Doleck, Tenzin, Lajoie, Susanne P., Bazelais, Paul
69Social Network analysising and academic performance: A longitudinal perspectiveAcademic performance; Bidirectional association; Longitudinal study; Social Network analysising sites; Temporal perspectiveto better understand the temporal association between SNS use and academic performance.Method.developmentotherSelf-reportedTimeSummativeBasic.statistical.analysisNo.learning.focus.outcome2019Doleck, Tenzin, Lajoie, Susanne P., Bazelais, Paul
70Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual Worldimmersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysisNoneGroup.comparisongame-based learningCustomized.log.dataEventGroup.event.patternCluster.analysisLearning.indicators2019Reilly, Joseph M, Dede, Chris
70Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual Worldimmersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysisNoneGroup.comparisongame-based learningCustomized.log.dataEventGroup.event.patternVisualization.analysisLearning.indicators2019Reilly, Joseph M, Dede, Chris
70Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual Worldimmersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysisNoneGroup.comparisongame-based learningCustomized.log.dataTrace-otherGroup.event.patternCluster.analysisLearning.indicators2019Reilly, Joseph M, Dede, Chris
70Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual Worldimmersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysisNoneGroup.comparisongame-based learningCustomized.log.dataTrace-otherGroup.event.patternVisualization.analysisLearning.indicators2019Reilly, Joseph M, Dede, Chris
70Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual Worldimmersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysisNoneGroup.comparisongame-based learningSelf-reportedEventGroup.event.patternCluster.analysisLearning.indicators2019Reilly, Joseph M, Dede, Chris
70Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual Worldimmersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysisNoneGroup.comparisongame-based learningSelf-reportedEventGroup.event.patternVisualization.analysisLearning.indicators2019Reilly, Joseph M, Dede, Chris
70Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual Worldimmersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysisNoneGroup.comparisongame-based learningSelf-reportedTrace-otherGroup.event.patternCluster.analysisLearning.indicators2019Reilly, Joseph M, Dede, Chris
70Differences in Student Trajectories via Filtered Time Series Analysis in an Immersive Virtual Worldimmersive virtual world; learning analytics; log file analysis; scientific inquiry; time-series analysisNoneGroup.comparisongame-based learningSelf-reportedTrace-otherGroup.event.patternVisualization.analysisLearning.indicators2019Reilly, Joseph M, Dede, Chris
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataEventTransitional.patternProcess.miningCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataEventTransitional.patternBasic.statistical.analysisLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataEventTransitional.patternBasic.statistical.analysisCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataEventTransitional.patternVisualization.analysisLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataEventTransitional.patternVisualization.analysisCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataEventSummativeProcess.miningLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataEventSummativeProcess.miningCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataEventSummativeBasic.statistical.analysisLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataEventSummativeBasic.statistical.analysisCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataEventSummativeVisualization.analysisLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataEventSummativeVisualization.analysisCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternProcess.miningLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternProcess.miningCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternBasic.statistical.analysisLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternBasic.statistical.analysisCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternVisualization.analysisLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternVisualization.analysisCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataTrace-forumSummativeProcess.miningLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataTrace-forumSummativeProcess.miningCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataTrace-forumSummativeBasic.statistical.analysisLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataTrace-forumSummativeBasic.statistical.analysisCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataTrace-forumSummativeVisualization.analysisLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLms.log.dataTrace-forumSummativeVisualization.analysisCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productEventTransitional.patternProcess.miningLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productEventTransitional.patternProcess.miningCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productEventTransitional.patternBasic.statistical.analysisLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productEventTransitional.patternBasic.statistical.analysisCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productEventTransitional.patternVisualization.analysisLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productEventTransitional.patternVisualization.analysisCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productEventSummativeProcess.miningLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productEventSummativeProcess.miningCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productEventSummativeBasic.statistical.analysisLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productEventSummativeBasic.statistical.analysisCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productEventSummativeVisualization.analysisLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productEventSummativeVisualization.analysisCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-forumTransitional.patternProcess.miningLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-forumTransitional.patternProcess.miningCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-forumTransitional.patternBasic.statistical.analysisLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-forumTransitional.patternBasic.statistical.analysisCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-forumTransitional.patternVisualization.analysisLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-forumTransitional.patternVisualization.analysisCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-forumSummativeProcess.miningLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-forumSummativeProcess.miningCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-forumSummativeBasic.statistical.analysisLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-forumSummativeBasic.statistical.analysisCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-forumSummativeVisualization.analysisLearning.indicators2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
71Uncovering the sequential patterns in transformative and non-transformative discourse during collaborative inquiry learningCollaborative work; Educational data mining; Learning analytics; Sequential analysis; Transformative discourseNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-forumSummativeVisualization.analysisCourse.design2019Zhu, Gaoxia, Xing, Wanli, Popov, Vitaliy
72Augmenting Knowledge Tracing by Considering Forgetting Behaviordeep neural Network analysis; forgetting behavior; knowledge tracingWe propose a knowledge tracing model that extends the DKT model to consider both a learning sequence and the forgetting behavior by explicitly modeling the forgetting behavior using multiple features. We have conducted experiments showing that our proposed model outperforms conventional methods in terms of the predictive performance on the knowledge tracing datasets.We have also examined how the combination of multiple types of forgetting information influences the performance.Method.developmentknowledge tracingLms.log.dataEventOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2019Nagatani, Koki, Zhang, Qian, Sato, Masahiro, Chen, Yan-Ying, Chen, Francine, Ohkuma, Tomoko
72Augmenting Knowledge Tracing by Considering Forgetting Behaviordeep neural Network analysis; forgetting behavior; knowledge tracingWe propose a knowledge tracing model that extends the DKT model to consider both a learning sequence and the forgetting behavior by explicitly modeling the forgetting behavior using multiple features. We have conducted experiments showing that our proposed model outperforms conventional methods in terms of the predictive performance on the knowledge tracing datasets.We have also examined how the combination of multiple types of forgetting information influences the performance.Method.developmentknowledge tracingLms.log.dataTimeOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2019Nagatani, Koki, Zhang, Qian, Sato, Masahiro, Chen, Yan-Ying, Chen, Francine, Ohkuma, Tomoko
72Augmenting Knowledge Tracing by Considering Forgetting Behaviordeep neural Network analysis; forgetting behavior; knowledge tracingWe propose a knowledge tracing model that extends the DKT model to consider both a learning sequence and the forgetting behavior by explicitly modeling the forgetting behavior using multiple features. We have conducted experiments showing that our proposed model outperforms conventional methods in terms of the predictive performance on the knowledge tracing datasets.We have also examined how the combination of multiple types of forgetting information influences the performance.Method.developmentknowledge tracingLms.log.dataTrace-quizOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2019Nagatani, Koki, Zhang, Qian, Sato, Masahiro, Chen, Yan-Ying, Chen, Francine, Ohkuma, Tomoko
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherLms.log.dataEventGroup.event.patternCluster.analysisCourse.design2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherLms.log.dataEventGroup.event.patternCluster.analysisFeedback2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherLms.log.dataEventGroup.event.patternVisualization.analysisCourse.design2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherLms.log.dataEventGroup.event.patternVisualization.analysisFeedback2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherLms.log.dataTimeGroup.event.patternCluster.analysisCourse.design2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherLms.log.dataTimeGroup.event.patternCluster.analysisFeedback2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherLms.log.dataTimeGroup.event.patternVisualization.analysisCourse.design2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherLms.log.dataTimeGroup.event.patternVisualization.analysisFeedback2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherLms.log.dataTrace-feedbackGroup.event.patternCluster.analysisCourse.design2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherLms.log.dataTrace-feedbackGroup.event.patternCluster.analysisFeedback2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherLms.log.dataTrace-feedbackGroup.event.patternVisualization.analysisCourse.design2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherLms.log.dataTrace-feedbackGroup.event.patternVisualization.analysisFeedback2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherLms.log.dataTrace-otherGroup.event.patternCluster.analysisCourse.design2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherLms.log.dataTrace-otherGroup.event.patternCluster.analysisFeedback2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherLms.log.dataTrace-otherGroup.event.patternVisualization.analysisCourse.design2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherLms.log.dataTrace-otherGroup.event.patternVisualization.analysisFeedback2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherPerformance.measuresEventGroup.event.patternCluster.analysisCourse.design2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherPerformance.measuresEventGroup.event.patternCluster.analysisFeedback2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherPerformance.measuresEventGroup.event.patternVisualization.analysisCourse.design2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherPerformance.measuresEventGroup.event.patternVisualization.analysisFeedback2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherPerformance.measuresTimeGroup.event.patternCluster.analysisCourse.design2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherPerformance.measuresTimeGroup.event.patternCluster.analysisFeedback2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherPerformance.measuresTimeGroup.event.patternVisualization.analysisCourse.design2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherPerformance.measuresTimeGroup.event.patternVisualization.analysisFeedback2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherPerformance.measuresTrace-feedbackGroup.event.patternCluster.analysisCourse.design2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherPerformance.measuresTrace-feedbackGroup.event.patternCluster.analysisFeedback2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherPerformance.measuresTrace-feedbackGroup.event.patternVisualization.analysisCourse.design2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherPerformance.measuresTrace-feedbackGroup.event.patternVisualization.analysisFeedback2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherPerformance.measuresTrace-otherGroup.event.patternCluster.analysisCourse.design2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherPerformance.measuresTrace-otherGroup.event.patternCluster.analysisFeedback2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherPerformance.measuresTrace-otherGroup.event.patternVisualization.analysisCourse.design2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
73Data-driven unsupervised Cluster analysis of online learner behaviourExperimental; Neurosciences; Psychology; Social Sciences; time-seriesNoneMethod.developmentotherPerformance.measuresTrace-otherGroup.event.patternVisualization.analysisFeedback2019Peach, Robert L, Yaliraki, Sophia N, Lefevre, David, Barahona, Mauricio
74Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schoolsITS; exploratory learning; external representation; feedback; maths educationNoneNon-srl.indicators.identificationotherLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2019Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
74Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schoolsITS; exploratory learning; external representation; feedback; maths educationNoneNon-srl.indicators.identificationotherLms.log.dataEventTransitional.patternProcess.miningFeedback2019Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
74Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schoolsITS; exploratory learning; external representation; feedback; maths educationNoneNon-srl.indicators.identificationotherLms.log.dataTimeTransitional.patternProcess.miningLearning.indicators2019Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
74Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schoolsITS; exploratory learning; external representation; feedback; maths educationNoneNon-srl.indicators.identificationotherLms.log.dataTimeTransitional.patternProcess.miningFeedback2019Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
74Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schoolsITS; exploratory learning; external representation; feedback; maths educationNoneNon-srl.indicators.identificationotherLms.log.dataTrace-feedbackTransitional.patternProcess.miningLearning.indicators2019Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
74Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schoolsITS; exploratory learning; external representation; feedback; maths educationNoneNon-srl.indicators.identificationotherLms.log.dataTrace-feedbackTransitional.patternProcess.miningFeedback2019Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
74Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schoolsITS; exploratory learning; external representation; feedback; maths educationNoneNon-srl.indicators.identificationotherLms.log.dataTrace-otherTransitional.patternProcess.miningLearning.indicators2019Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
74Interaction patterns in exploratory learning environments for mathematics: a sequential analysis of feedback and external representations in Chinese schoolsITS; exploratory learning; external representation; feedback; maths educationNoneNon-srl.indicators.identificationotherLms.log.dataTrace-otherTransitional.patternProcess.miningFeedback2019Zhang, Jingjing, Gao, Ming, Holmes, Wayne, Mavrikis, Manolis, Ma, Ning
75Learning anytime, anywhere: a spatio-temporal analysis for online learningOnline course; anytime anywhere; learning performance; spatio-temporal analysisWhat are student’s temporal and spatial characteristics in an online course?What type(s) of temporal and spatial characteristics perform better in an online course? Is there any connection between student demographics and a specific temporal–spatial pattern?Group.comparisonNoneMultimodalEventSummativeBasic.statistical.analysisNo.learning.focus.outcome2019Du, Xu, Zhang, Mingyan, Shelton, Brett E., Hung, Jui Long
75Learning anytime, anywhere: a spatio-temporal analysis for online learningOnline course; anytime anywhere; learning performance; spatio-temporal analysisWhat are student’s temporal and spatial characteristics in an online course?What type(s) of temporal and spatial characteristics perform better in an online course? Is there any connection between student demographics and a specific temporal–spatial pattern?Group.comparisonNoneMultimodalTimeSummativeBasic.statistical.analysisNo.learning.focus.outcome2019Du, Xu, Zhang, Mingyan, Shelton, Brett E., Hung, Jui Long
76Investigating students' interaction patterns and dynamic learning sentiments in online discussionsDynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?Exploring.socio-dynamicsaffective learningLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2019Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76Investigating students' interaction patterns and dynamic learning sentiments in online discussionsDynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?Exploring.socio-dynamicsaffective learningLms.log.dataEventTransitional.patternVisualization.analysisLearning.indicators2019Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76Investigating students' interaction patterns and dynamic learning sentiments in online discussionsDynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?Exploring.socio-dynamicsaffective learningLms.log.dataTrace-otherTransitional.patternProcess.miningLearning.indicators2019Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76Investigating students' interaction patterns and dynamic learning sentiments in online discussionsDynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?Exploring.socio-dynamicsaffective learningLms.log.dataTrace-otherTransitional.patternVisualization.analysisLearning.indicators2019Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76Investigating students' interaction patterns and dynamic learning sentiments in online discussionsDynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?Exploring.socio-dynamicsaffective learningLms.log.dataTrace-forumTransitional.patternProcess.miningLearning.indicators2019Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76Investigating students' interaction patterns and dynamic learning sentiments in online discussionsDynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?Exploring.socio-dynamicsaffective learningLms.log.dataTrace-forumTransitional.patternVisualization.analysisLearning.indicators2019Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76Investigating students' interaction patterns and dynamic learning sentiments in online discussionsDynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?Exploring.socio-dynamicsaffective learningLearning.productEventTransitional.patternProcess.miningLearning.indicators2019Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76Investigating students' interaction patterns and dynamic learning sentiments in online discussionsDynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?Exploring.socio-dynamicsaffective learningLearning.productEventTransitional.patternVisualization.analysisLearning.indicators2019Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76Investigating students' interaction patterns and dynamic learning sentiments in online discussionsDynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?Exploring.socio-dynamicsaffective learningLearning.productTrace-otherTransitional.patternProcess.miningLearning.indicators2019Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76Investigating students' interaction patterns and dynamic learning sentiments in online discussionsDynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?Exploring.socio-dynamicsaffective learningLearning.productTrace-otherTransitional.patternVisualization.analysisLearning.indicators2019Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76Investigating students' interaction patterns and dynamic learning sentiments in online discussionsDynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?Exploring.socio-dynamicsaffective learningLearning.productTrace-forumTransitional.patternProcess.miningLearning.indicators2019Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
76Investigating students' interaction patterns and dynamic learning sentiments in online discussionsDynamic learning emotions; Interaction patterns; Lag sequential analysis; Online learning discussions(1) What are learning sentiments emerging from the process of online discussion? (2) What interaction patterns do students demonstrate from the dynamic learning sentiment perspective? (3) What are differences in learning sentiments and interaction patterns arising in individual-oriented and group-oriented task discussions?Exploring.socio-dynamicsaffective learningLearning.productTrace-forumTransitional.patternVisualization.analysisLearning.indicators2019Huang, Chang-Qin, Han, Zhong-Mei, Li, Ming-Xi, Jong, Morris Siu-yung, Tsai, Chin-Chung
77Transfer Learning Using Representation Learning in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?Method.developmentNoneLms.log.dataEventSummativeOther.predictions.modelsNo.learning.focus.outcome2019Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77Transfer Learning Using Representation Learning in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?Method.developmentNoneLms.log.dataEventSummativeVisualization.analysisNo.learning.focus.outcome2019Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77Transfer Learning Using Representation Learning in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?Method.developmentNoneLms.log.dataTimeSummativeOther.predictions.modelsNo.learning.focus.outcome2019Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77Transfer Learning Using Representation Learning in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?Method.developmentNoneLms.log.dataTimeSummativeVisualization.analysisNo.learning.focus.outcome2019Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77Transfer Learning Using Representation Learning in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?Method.developmentNonePerformance.measuresEventSummativeOther.predictions.modelsNo.learning.focus.outcome2019Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77Transfer Learning Using Representation Learning in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?Method.developmentNonePerformance.measuresEventSummativeVisualization.analysisNo.learning.focus.outcome2019Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77Transfer Learning Using Representation Learning in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?Method.developmentNonePerformance.measuresTimeSummativeOther.predictions.modelsNo.learning.focus.outcome2019Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77Transfer Learning Using Representation Learning in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?Method.developmentNonePerformance.measuresTimeSummativeVisualization.analysisNo.learning.focus.outcome2019Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77Transfer Learning Using Representation Learning in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?At-risk.student.identificationNoneLms.log.dataEventSummativeOther.predictions.modelsNo.learning.focus.outcome2019Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77Transfer Learning Using Representation Learning in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?At-risk.student.identificationNoneLms.log.dataEventSummativeVisualization.analysisNo.learning.focus.outcome2019Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77Transfer Learning Using Representation Learning in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?At-risk.student.identificationNoneLms.log.dataTimeSummativeOther.predictions.modelsNo.learning.focus.outcome2019Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77Transfer Learning Using Representation Learning in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?At-risk.student.identificationNoneLms.log.dataTimeSummativeVisualization.analysisNo.learning.focus.outcome2019Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77Transfer Learning Using Representation Learning in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?At-risk.student.identificationNonePerformance.measuresEventSummativeOther.predictions.modelsNo.learning.focus.outcome2019Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77Transfer Learning Using Representation Learning in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?At-risk.student.identificationNonePerformance.measuresEventSummativeVisualization.analysisNo.learning.focus.outcome2019Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77Transfer Learning Using Representation Learning in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?At-risk.student.identificationNonePerformance.measuresTimeSummativeOther.predictions.modelsNo.learning.focus.outcome2019Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
77Transfer Learning Using Representation Learning in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Dropout Prediction; MOOC; Representation Learning; Transfer Learning(1) Does representation learning improve model transfer? We evaluate transferability within offerings for two courses and across two courses. (2) Can representation-based learning work from a universal, basic set of MOOC activity features as input? We test a time-series per student where the frequencies of a set of specific MOOC activity types are expressed per time unit. (3) Can transfer learning improve recognition of minority groups? If we group similar students and transfer learning for each group independently, does predictive performance improve? (4) What are the embedded features that increase the transferability?At-risk.student.identificationNonePerformance.measuresTimeSummativeVisualization.analysisNo.learning.focus.outcome2019Ding, Mucong, Wang, Yanbang, Hemberg, Erik, O'Reilly, Una-May
78Knowledge Query Network for Knowledge Tracing: How Knowledge Interacts with SkillsDeep Learning; Domain Modeling; Educational Data Mining; Intelligent Tutoring Systems; Knowledge Modeling; Knowledge Tracing; Learner Modeling; Learning Analytics; Massive Open Online CoursesNoneMethod.developmentNoneLms.log.dataTimeNoneOther.predictions.modelsNo.learning.focus.outcome2019Lee, Jinseok, Yeung, Dit-Yan
78Knowledge Query Network for Knowledge Tracing: How Knowledge Interacts with SkillsDeep Learning; Domain Modeling; Educational Data Mining; Intelligent Tutoring Systems; Knowledge Modeling; Knowledge Tracing; Learner Modeling; Learning Analytics; Massive Open Online CoursesNoneMethod.developmentNoneLms.log.dataEventNoneOther.predictions.modelsNo.learning.focus.outcome2019Lee, Jinseok, Yeung, Dit-Yan
78Knowledge Query Network for Knowledge Tracing: How Knowledge Interacts with SkillsDeep Learning; Domain Modeling; Educational Data Mining; Intelligent Tutoring Systems; Knowledge Modeling; Knowledge Tracing; Learner Modeling; Learning Analytics; Massive Open Online CoursesNoneMethod.developmentNonePerformance.measuresTimeNoneOther.predictions.modelsNo.learning.focus.outcome2019Lee, Jinseok, Yeung, Dit-Yan
78Knowledge Query Network for Knowledge Tracing: How Knowledge Interacts with SkillsDeep Learning; Domain Modeling; Educational Data Mining; Intelligent Tutoring Systems; Knowledge Modeling; Knowledge Tracing; Learner Modeling; Learning Analytics; Massive Open Online CoursesNoneMethod.developmentNonePerformance.measuresEventNoneOther.predictions.modelsNo.learning.focus.outcome2019Lee, Jinseok, Yeung, Dit-Yan
79The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning coursescourse perfomance; blended learning; temporal patternHow do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement?Non-srl.indicators.identificationotherLms.log.dataEventSummativeCluster.analysisTime.on.learning2019van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning coursescourse perfomance; blended learning; temporal patternHow do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement?Non-srl.indicators.identificationotherLms.log.dataTrace-quizSummativeCluster.analysisTime.on.learning2019van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning coursescourse perfomance; blended learning; temporal patternHow do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement?Non-srl.indicators.identificationotherLms.log.dataTrace-readingSummativeCluster.analysisTime.on.learning2019van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning coursescourse perfomance; blended learning; temporal patternHow do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement?Non-srl.indicators.identificationotherLms.log.dataTrace-exerciseSummativeCluster.analysisTime.on.learning2019van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning coursescourse perfomance; blended learning; temporal patternHow do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement?Non-srl.indicators.identificationotherLms.log.dataTrace-forumSummativeCluster.analysisTime.on.learning2019van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning coursescourse perfomance; blended learning; temporal patternHow do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement?Non-srl.indicators.identificationotherPerformance.measuresEventSummativeCluster.analysisTime.on.learning2019van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning coursescourse perfomance; blended learning; temporal patternHow do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement?Non-srl.indicators.identificationotherPerformance.measuresTrace-quizSummativeCluster.analysisTime.on.learning2019van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning coursescourse perfomance; blended learning; temporal patternHow do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement?Non-srl.indicators.identificationotherPerformance.measuresTrace-readingSummativeCluster.analysisTime.on.learning2019van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning coursescourse perfomance; blended learning; temporal patternHow do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement?Non-srl.indicators.identificationotherPerformance.measuresTrace-exerciseSummativeCluster.analysisTime.on.learning2019van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
79The role of temporal patterns in students' behavior for predicting course performance: A comparison of two blended learning coursescourse perfomance; blended learning; temporal patternHow do a flipped classroom model and enhanced hybrid course model compare concerning the influence oftemporal patterns ofactivity and type ofactivity on student achievement?Non-srl.indicators.identificationotherPerformance.measuresTrace-forumSummativeCluster.analysisTime.on.learning2019van Leeuwen, Anouschka, Bos, Nynke, van Ravenswaaij, Heleen, van Oostenrijk, Jurgen
80Mining Activity Log Data to Predict Student's Outcome in a CourseClassification; Education data mining; Learning analytics; predictionNoneAt-risk.student.identificationNoneLms.log.dataEventSummativeCluster.analysisNo.learning.focus.outcome2019Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi
80Mining Activity Log Data to Predict Student's Outcome in a CourseClassification; Education data mining; Learning analytics; predictionNoneAt-risk.student.identificationNoneLms.log.dataEventSummativeOther.predictions.modelsNo.learning.focus.outcome2019Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi
80Mining Activity Log Data to Predict Student's Outcome in a CourseClassification; Education data mining; Learning analytics; predictionNoneAt-risk.student.identificationNoneLearner.characteristicsEventSummativeCluster.analysisNo.learning.focus.outcome2019Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi
80Mining Activity Log Data to Predict Student's Outcome in a CourseClassification; Education data mining; Learning analytics; predictionNoneAt-risk.student.identificationNoneLearner.characteristicsEventSummativeOther.predictions.modelsNo.learning.focus.outcome2019Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi
80Mining Activity Log Data to Predict Student's Outcome in a CourseClassification; Education data mining; Learning analytics; predictionNoneAt-risk.student.identificationNonePerformance.measuresEventSummativeCluster.analysisNo.learning.focus.outcome2019Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi
80Mining Activity Log Data to Predict Student's Outcome in a CourseClassification; Education data mining; Learning analytics; predictionNoneAt-risk.student.identificationNonePerformance.measuresEventSummativeOther.predictions.modelsNo.learning.focus.outcome2019Umer, Rahila, Mathrani, Anuradha, Susnjak, Teo, Lim, Suriadi
81Understanding the process of teachers’ technology adoption with a dynamic analytical modelTeachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process researchthe current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns.Non-srl.indicators.identificationfeedback engagementLms.log.dataEventSummativeBasic.statistical.analysisFeedback2019Zheng, Longwei, Gibson, David, Gu, Xiaoqing
81Understanding the process of teachers’ technology adoption with a dynamic analytical modelTeachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process researchthe current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns.Non-srl.indicators.identificationfeedback engagementLms.log.dataTrace-feedbackSummativeBasic.statistical.analysisFeedback2019Zheng, Longwei, Gibson, David, Gu, Xiaoqing
81Understanding the process of teachers’ technology adoption with a dynamic analytical modelTeachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process researchthe current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns.Non-srl.indicators.identificationfeedback engagementLms.log.dataTrace-otherSummativeBasic.statistical.analysisFeedback2019Zheng, Longwei, Gibson, David, Gu, Xiaoqing
81Understanding the process of teachers’ technology adoption with a dynamic analytical modelTeachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process researchthe current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns.Method.developmentfeedback engagementLms.log.dataEventSummativeBasic.statistical.analysisFeedback2019Zheng, Longwei, Gibson, David, Gu, Xiaoqing
81Understanding the process of teachers’ technology adoption with a dynamic analytical modelTeachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process researchthe current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns.Method.developmentfeedback engagementLms.log.dataTrace-feedbackSummativeBasic.statistical.analysisFeedback2019Zheng, Longwei, Gibson, David, Gu, Xiaoqing
81Understanding the process of teachers’ technology adoption with a dynamic analytical modelTeachers’ technology adoption; dynamic model; hidden Markov model; innovation adoption; process researchthe current work explores the phenomenon using an NHMM to consider the possible factors that may influence dynamics and examines how these factors impact the stability or changes in adoption patterns.Method.developmentfeedback engagementLms.log.dataTrace-otherSummativeBasic.statistical.analysisFeedback2019Zheng, Longwei, Gibson, David, Gu, Xiaoqing
82How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining studyCollaborative learning; Hypermedia; Primary school; Process.mining; SSRLRQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2019Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining studyCollaborative learning; Hypermedia; Primary school; Process.mining; SSRLRQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataEventTransitional.patternVisualization.analysisLearning.indicators2019Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining studyCollaborative learning; Hypermedia; Primary school; Process.mining; SSRLRQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataTrace-readingTransitional.patternProcess.miningLearning.indicators2019Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining studyCollaborative learning; Hypermedia; Primary school; Process.mining; SSRLRQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataTrace-readingTransitional.patternVisualization.analysisLearning.indicators2019Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining studyCollaborative learning; Hypermedia; Primary school; Process.mining; SSRLRQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataTrace-feedbackTransitional.patternProcess.miningLearning.indicators2019Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining studyCollaborative learning; Hypermedia; Primary school; Process.mining; SSRLRQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataTrace-feedbackTransitional.patternVisualization.analysisLearning.indicators2019Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining studyCollaborative learning; Hypermedia; Primary school; Process.mining; SSRLRQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternProcess.miningLearning.indicators2019Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining studyCollaborative learning; Hypermedia; Primary school; Process.mining; SSRLRQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternVisualization.analysisLearning.indicators2019Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining studyCollaborative learning; Hypermedia; Primary school; Process.mining; SSRLRQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataTrace-otherTransitional.patternProcess.miningLearning.indicators2019Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining studyCollaborative learning; Hypermedia; Primary school; Process.mining; SSRLRQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataTrace-otherTransitional.patternVisualization.analysisLearning.indicators2019Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining studyCollaborative learning; Hypermedia; Primary school; Process.mining; SSRLRQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2019Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining studyCollaborative learning; Hypermedia; Primary school; Process.mining; SSRLRQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataEventTransitional.patternVisualization.analysisLearning.indicators2019Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining studyCollaborative learning; Hypermedia; Primary school; Process.mining; SSRLRQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataTrace-readingTransitional.patternProcess.miningLearning.indicators2019Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining studyCollaborative learning; Hypermedia; Primary school; Process.mining; SSRLRQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataTrace-readingTransitional.patternVisualization.analysisLearning.indicators2019Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining studyCollaborative learning; Hypermedia; Primary school; Process.mining; SSRLRQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataTrace-feedbackTransitional.patternProcess.miningLearning.indicators2019Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining studyCollaborative learning; Hypermedia; Primary school; Process.mining; SSRLRQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataTrace-feedbackTransitional.patternVisualization.analysisLearning.indicators2019Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining studyCollaborative learning; Hypermedia; Primary school; Process.mining; SSRLRQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternProcess.miningLearning.indicators2019Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining studyCollaborative learning; Hypermedia; Primary school; Process.mining; SSRLRQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternVisualization.analysisLearning.indicators2019Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining studyCollaborative learning; Hypermedia; Primary school; Process.mining; SSRLRQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataTrace-otherTransitional.patternProcess.miningLearning.indicators2019Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
82How social challenges affect children’s regulation and assignment quality in hypermedia: a process mining studyCollaborative learning; Hypermedia; Primary school; Process.mining; SSRLRQ 1: To what extent do low and high social challenge dyads differ in the quality of their written assignment? RQ 2: How do low and high social challenge dyads differ in terms of the frequency of their cognitive, metacognitive, relational, and off-task activities? RQ 3: How do low and high social challenge dyads differ in terms of the sequential pattern of their cognitive, metacognitive, relational, and off-task activities?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataTrace-otherTransitional.patternVisualization.analysisLearning.indicators2019Paans, Cindy, Onan, Erdem, Molenaar, Inge, Verhoeven, Ludo, Segers, Eliane
83Exploring how students interact with guidance in a physics simulation: evidence from eye-movement and log data analysesPhysics; eye-tracking; inquiry; learning analytics; simulationNoneNon-srl.indicators.identificationotherMultimodalEventTransitional.patternProcess.miningLearning.indicators2019Chiou, Guo Li, Hsu, Chung Yuan, Tsai, Meng Jung
83Exploring how students interact with guidance in a physics simulation: evidence from eye-movement and log data analysesPhysics; eye-tracking; inquiry; learning analytics; simulationNoneNon-srl.indicators.identificationotherMultimodalTimeTransitional.patternProcess.miningLearning.indicators2019Chiou, Guo Li, Hsu, Chung Yuan, Tsai, Meng Jung
83Exploring how students interact with guidance in a physics simulation: evidence from eye-movement and log data analysesPhysics; eye-tracking; inquiry; learning analytics; simulationNoneGroup.comparisonotherMultimodalEventTransitional.patternProcess.miningLearning.indicators2019Chiou, Guo Li, Hsu, Chung Yuan, Tsai, Meng Jung
83Exploring how students interact with guidance in a physics simulation: evidence from eye-movement and log data analysesPhysics; eye-tracking; inquiry; learning analytics; simulationNoneGroup.comparisonotherMultimodalTimeTransitional.patternProcess.miningLearning.indicators2019Chiou, Guo Li, Hsu, Chung Yuan, Tsai, Meng Jung
84Recognizing patterns of student’s modeling behaviour patterns via process miningStudent behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineeringApply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.Method.developmentNoneCustomized.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2019Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84Recognizing patterns of student’s modeling behaviour patterns via process miningStudent behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineeringApply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.Method.developmentNoneCustomized.log.dataEventEvent.sequenceProcess.miningLearning.indicators2019Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84Recognizing patterns of student’s modeling behaviour patterns via process miningStudent behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineeringApply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.Method.developmentNoneCustomized.log.dataEventSummativeFrequent.sequence.miningLearning.indicators2019Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84Recognizing patterns of student’s modeling behaviour patterns via process miningStudent behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineeringApply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.Method.developmentNoneCustomized.log.dataEventSummativeProcess.miningLearning.indicators2019Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84Recognizing patterns of student’s modeling behaviour patterns via process miningStudent behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineeringApply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.Method.developmentNoneCustomized.log.dataTrace-otherEvent.sequenceFrequent.sequence.miningLearning.indicators2019Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84Recognizing patterns of student’s modeling behaviour patterns via process miningStudent behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineeringApply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.Method.developmentNoneCustomized.log.dataTrace-otherEvent.sequenceProcess.miningLearning.indicators2019Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84Recognizing patterns of student’s modeling behaviour patterns via process miningStudent behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineeringApply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.Method.developmentNoneCustomized.log.dataTrace-otherSummativeFrequent.sequence.miningLearning.indicators2019Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84Recognizing patterns of student’s modeling behaviour patterns via process miningStudent behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineeringApply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.Method.developmentNoneCustomized.log.dataTrace-otherSummativeProcess.miningLearning.indicators2019Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84Recognizing patterns of student’s modeling behaviour patterns via process miningStudent behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineeringApply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.Method.developmentNonePerformance.measuresEventEvent.sequenceFrequent.sequence.miningLearning.indicators2019Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84Recognizing patterns of student’s modeling behaviour patterns via process miningStudent behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineeringApply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.Method.developmentNonePerformance.measuresEventEvent.sequenceProcess.miningLearning.indicators2019Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84Recognizing patterns of student’s modeling behaviour patterns via process miningStudent behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineeringApply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.Method.developmentNonePerformance.measuresEventSummativeFrequent.sequence.miningLearning.indicators2019Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84Recognizing patterns of student’s modeling behaviour patterns via process miningStudent behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineeringApply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.Method.developmentNonePerformance.measuresEventSummativeProcess.miningLearning.indicators2019Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84Recognizing patterns of student’s modeling behaviour patterns via process miningStudent behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineeringApply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.Method.developmentNonePerformance.measuresTrace-otherEvent.sequenceFrequent.sequence.miningLearning.indicators2019Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84Recognizing patterns of student’s modeling behaviour patterns via process miningStudent behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineeringApply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.Method.developmentNonePerformance.measuresTrace-otherEvent.sequenceProcess.miningLearning.indicators2019Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84Recognizing patterns of student’s modeling behaviour patterns via process miningStudent behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineeringApply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.Method.developmentNonePerformance.measuresTrace-otherSummativeFrequent.sequence.miningLearning.indicators2019Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
84Recognizing patterns of student’s modeling behaviour patterns via process miningStudent behavior analysis; Learning effect evaluation; Frequent sequential pattern mining; Feature engineeringApply process mining methods to action sequences with the purpose of revealing general characteristics. Associate analysis between process mining results and numeric evaluation values in order to understand student’s online modeling behavior habit. Carry out a comprehensive case study and figure out insightful conclusions.Method.developmentNonePerformance.measuresTrace-otherSummativeProcess.miningLearning.indicators2019Wang, Yu Yihan, Li, Tong, Geng, Congkai, Wang, Yu Yihan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneMultimodalEventEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneMultimodalEventEvent.sequenceCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneMultimodalEventGroup.event.patternFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneMultimodalEventGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneMultimodalTrace-forumEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneMultimodalTrace-forumEvent.sequenceCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneMultimodalTrace-forumGroup.event.patternFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneMultimodalTrace-forumGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneMultimodalTrace-quizEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneMultimodalTrace-quizEvent.sequenceCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneMultimodalTrace-quizGroup.event.patternFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneMultimodalTrace-quizGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneMultimodalTrace-readingEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneMultimodalTrace-readingEvent.sequenceCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneMultimodalTrace-readingGroup.event.patternFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneMultimodalTrace-readingGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneMultimodalTrace-otherEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneMultimodalTrace-otherEvent.sequenceCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneMultimodalTrace-otherGroup.event.patternFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneMultimodalTrace-otherGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneLms.log.dataEventEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneLms.log.dataEventEvent.sequenceCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneLms.log.dataEventGroup.event.patternFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneLms.log.dataEventGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneLms.log.dataTrace-forumEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneLms.log.dataTrace-forumEvent.sequenceCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneLms.log.dataTrace-forumGroup.event.patternFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneLms.log.dataTrace-forumGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneLms.log.dataTrace-quizEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneLms.log.dataTrace-quizEvent.sequenceCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneLms.log.dataTrace-quizGroup.event.patternFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneLms.log.dataTrace-quizGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneLms.log.dataTrace-readingEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneLms.log.dataTrace-readingEvent.sequenceCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneLms.log.dataTrace-readingGroup.event.patternFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneLms.log.dataTrace-readingGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneLms.log.dataTrace-otherEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneLms.log.dataTrace-otherEvent.sequenceCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneLms.log.dataTrace-otherGroup.event.patternFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?Method.developmentNoneLms.log.dataTrace-otherGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneMultimodalEventEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneMultimodalEventEvent.sequenceCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneMultimodalEventGroup.event.patternFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneMultimodalEventGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneMultimodalTrace-forumEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneMultimodalTrace-forumEvent.sequenceCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneMultimodalTrace-forumGroup.event.patternFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneMultimodalTrace-forumGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneMultimodalTrace-quizEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneMultimodalTrace-quizEvent.sequenceCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneMultimodalTrace-quizGroup.event.patternFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneMultimodalTrace-quizGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneMultimodalTrace-readingEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneMultimodalTrace-readingEvent.sequenceCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneMultimodalTrace-readingGroup.event.patternFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneMultimodalTrace-readingGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneMultimodalTrace-otherEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneMultimodalTrace-otherEvent.sequenceCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneMultimodalTrace-otherGroup.event.patternFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneMultimodalTrace-otherGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneLms.log.dataEventEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneLms.log.dataEventEvent.sequenceCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneLms.log.dataEventGroup.event.patternFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneLms.log.dataEventGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneLms.log.dataTrace-forumEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneLms.log.dataTrace-forumEvent.sequenceCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneLms.log.dataTrace-forumGroup.event.patternFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneLms.log.dataTrace-forumGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneLms.log.dataTrace-quizEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneLms.log.dataTrace-quizEvent.sequenceCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneLms.log.dataTrace-quizGroup.event.patternFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneLms.log.dataTrace-quizGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneLms.log.dataTrace-readingEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneLms.log.dataTrace-readingEvent.sequenceCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneLms.log.dataTrace-readingGroup.event.patternFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneLms.log.dataTrace-readingGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneLms.log.dataTrace-otherEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneLms.log.dataTrace-otherEvent.sequenceCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneLms.log.dataTrace-otherGroup.event.patternFrequent.sequence.miningNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
85On multi-device use: Using technological modality profiles to explain differences in students’ learningBlended learning; Learning analytics; Mobile Learning; Multi-device use; Online discussions; Trace Analysis(1) RQ1: Can we detect patterns in students’ use of multiple modalities that are indicative of their adopted technological modality strategy when using an LMS tool? If so, what kind of strategies emerge? (2) RQ2: Is there an association of the identified strategies with students’ performance in AODs and overall academic performance?At-risk.student.identificationNoneLms.log.dataTrace-otherGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Sher, Varshita, Hatala, Marek, Gavsevic, Dragan
86A learning analytics approach to investigating pre-service teachers’ change of concept of engagement in the flipped classroomBloom’s Taxonomy; cognitive style; concept of engagement; flipped classroom; learning analytics(1) What students’ concepts of engagement will determine their learning patterns in the flipped classroom?(2) Is there a difference in the behavioral patterns between the intuitive-style students and the analytical-style students in the flipped classroom?(3) Is there a difference in the achievements between the intuitive-style students and the analytical- style students in the flipped classroom?Non-srl.indicators.identificationotherLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2019Sun, Fu Rong, Hu, Hong Zhen, Wan, Rong Gen, Fu, Xiao, Wu, Shu Jing
86A learning analytics approach to investigating pre-service teachers’ change of concept of engagement in the flipped classroomBloom’s Taxonomy; cognitive style; concept of engagement; flipped classroom; learning analytics(1) What students’ concepts of engagement will determine their learning patterns in the flipped classroom?(2) Is there a difference in the behavioral patterns between the intuitive-style students and the analytical-style students in the flipped classroom?(3) Is there a difference in the achievements between the intuitive-style students and the analytical- style students in the flipped classroom?Non-srl.indicators.identificationotherLms.log.dataTrace-otherTransitional.patternProcess.miningLearning.indicators2019Sun, Fu Rong, Hu, Hong Zhen, Wan, Rong Gen, Fu, Xiao, Wu, Shu Jing
86A learning analytics approach to investigating pre-service teachers’ change of concept of engagement in the flipped classroomBloom’s Taxonomy; cognitive style; concept of engagement; flipped classroom; learning analytics(1) What students’ concepts of engagement will determine their learning patterns in the flipped classroom?(2) Is there a difference in the behavioral patterns between the intuitive-style students and the analytical-style students in the flipped classroom?(3) Is there a difference in the achievements between the intuitive-style students and the analytical- style students in the flipped classroom?Group.comparisonotherLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2019Sun, Fu Rong, Hu, Hong Zhen, Wan, Rong Gen, Fu, Xiao, Wu, Shu Jing
86A learning analytics approach to investigating pre-service teachers’ change of concept of engagement in the flipped classroomBloom’s Taxonomy; cognitive style; concept of engagement; flipped classroom; learning analytics(1) What students’ concepts of engagement will determine their learning patterns in the flipped classroom?(2) Is there a difference in the behavioral patterns between the intuitive-style students and the analytical-style students in the flipped classroom?(3) Is there a difference in the achievements between the intuitive-style students and the analytical- style students in the flipped classroom?Group.comparisonotherLms.log.dataTrace-otherTransitional.patternProcess.miningLearning.indicators2019Sun, Fu Rong, Hu, Hong Zhen, Wan, Rong Gen, Fu, Xiao, Wu, Shu Jing
87User behavior pattern detection in unstructured processes – a learning management system case studyLearning analytics; gamification; learning management systems; pattern detection; process mining; spaghetti processescan we automatically identify recurring user-level behavior patterns and perform user Cluster analysis based on these patterns?Method.developmentgame-based learningCustomized.log.dataEventTransitional.patternProcess.miningLearning.indicators2019Codish, David, Rabin, Eyal, Ravid, Gilad
87User behavior pattern detection in unstructured processes – a learning management system case studyLearning analytics; gamification; learning management systems; pattern detection; process mining; spaghetti processescan we automatically identify recurring user-level behavior patterns and perform user Cluster analysis based on these patterns?Method.developmentgame-based learningCustomized.log.dataEventTransitional.patternCluster.analysisLearning.indicators2019Codish, David, Rabin, Eyal, Ravid, Gilad
87User behavior pattern detection in unstructured processes – a learning management system case studyLearning analytics; gamification; learning management systems; pattern detection; process mining; spaghetti processescan we automatically identify recurring user-level behavior patterns and perform user Cluster analysis based on these patterns?Method.developmentgame-based learningCustomized.log.dataEventTransitional.patternVisualization.analysisLearning.indicators2019Codish, David, Rabin, Eyal, Ravid, Gilad
88Visual behavior and self-efficacy of game playing: an eye movement analysisEye tracking; game self-efficacy; game-based learning; lag sequential analysis; visual behaviorthis study aimed to explore whether players with different game self-efficacy have different game performance and how these differences reflect their strategies used during the game. We drew a hypothesis that players with higher game self-efficacy tended to have better game performance and to have different visual attention distributions and transition patterns during their gameplaying.Group.comparisonotherMultimodalEventTransitional.patternProcess.miningLearning.indicators2019Hsu, Chung Yuan, Chiou, Guo Li, Tsai, Meng Jung
88Visual behavior and self-efficacy of game playing: an eye movement analysisEye tracking; game self-efficacy; game-based learning; lag sequential analysis; visual behaviorthis study aimed to explore whether players with different game self-efficacy have different game performance and how these differences reflect their strategies used during the game. We drew a hypothesis that players with higher game self-efficacy tended to have better game performance and to have different visual attention distributions and transition patterns during their gameplaying.Group.comparisonotherSelf-reportedEventTransitional.patternProcess.miningLearning.indicators2019Hsu, Chung Yuan, Chiou, Guo Li, Tsai, Meng Jung
89Temporal emotion-aspect modeling for discovering what students are concerned about in online course forumsDiscussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?Non-srl.indicators.identificationaffective learningLms.log.dataEventSummativeContent.analysisLearning.indicators2019Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89Temporal emotion-aspect modeling for discovering what students are concerned about in online course forumsDiscussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?Non-srl.indicators.identificationaffective learningLms.log.dataEventSummativeBasic.statistical.analysisLearning.indicators2019Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89Temporal emotion-aspect modeling for discovering what students are concerned about in online course forumsDiscussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?Non-srl.indicators.identificationaffective learningLms.log.dataEventSummativeVisualization.analysisLearning.indicators2019Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89Temporal emotion-aspect modeling for discovering what students are concerned about in online course forumsDiscussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?Non-srl.indicators.identificationaffective learningLms.log.dataTimeSummativeContent.analysisLearning.indicators2019Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89Temporal emotion-aspect modeling for discovering what students are concerned about in online course forumsDiscussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?Non-srl.indicators.identificationaffective learningLms.log.dataTimeSummativeBasic.statistical.analysisLearning.indicators2019Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89Temporal emotion-aspect modeling for discovering what students are concerned about in online course forumsDiscussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?Non-srl.indicators.identificationaffective learningLms.log.dataTimeSummativeVisualization.analysisLearning.indicators2019Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89Temporal emotion-aspect modeling for discovering what students are concerned about in online course forumsDiscussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?Non-srl.indicators.identificationaffective learningLearning.productEventSummativeContent.analysisLearning.indicators2019Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89Temporal emotion-aspect modeling for discovering what students are concerned about in online course forumsDiscussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?Non-srl.indicators.identificationaffective learningLearning.productEventSummativeBasic.statistical.analysisLearning.indicators2019Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89Temporal emotion-aspect modeling for discovering what students are concerned about in online course forumsDiscussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?Non-srl.indicators.identificationaffective learningLearning.productEventSummativeVisualization.analysisLearning.indicators2019Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89Temporal emotion-aspect modeling for discovering what students are concerned about in online course forumsDiscussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?Non-srl.indicators.identificationaffective learningLearning.productTimeSummativeContent.analysisLearning.indicators2019Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89Temporal emotion-aspect modeling for discovering what students are concerned about in online course forumsDiscussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?Non-srl.indicators.identificationaffective learningLearning.productTimeSummativeBasic.statistical.analysisLearning.indicators2019Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
89Temporal emotion-aspect modeling for discovering what students are concerned about in online course forumsDiscussion forum; emotion-aspect evolution; emotional difference; learning analytics; temporal emotion-aspect model (TEAM)Compared with using Basic statistical analysise-of-art approaches, e.g. ASUM, can a better performance be achieved by TEAM? And what is the optimal number of aspects that can ensure the best performance of TEAM?2. What are the students’ most concerned aspects in terms of positive, negative and confused emotions from forum discussion?3. What are the evolutionary trends of the most significant emotion-aspect associations over the whole course progress among students?4. What are the differences between the high-, medium- and low-achieving groups in terms of evolutionary trends of emotion-aspect associations?Non-srl.indicators.identificationaffective learningLearning.productTimeSummativeVisualization.analysisLearning.indicators2019Liu, Zhi, Yang, Chongyang, R{\"u}dian, Sylvio, Liu, Sannyuya, Zhao, Liang, Wang, Tai
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLms.log.dataEventGroup.event.patternProcess.miningNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLms.log.dataEventGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLms.log.dataEventGroup.event.patternVisualization.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLms.log.dataEventTransitional.patternProcess.miningNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLms.log.dataEventTransitional.patternCluster.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLms.log.dataEventTransitional.patternVisualization.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLms.log.dataTimeGroup.event.patternProcess.miningNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLms.log.dataTimeGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLms.log.dataTimeGroup.event.patternVisualization.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLms.log.dataTimeTransitional.patternProcess.miningNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLms.log.dataTimeTransitional.patternCluster.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLms.log.dataTimeTransitional.patternVisualization.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNonePerformance.measuresEventGroup.event.patternProcess.miningNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNonePerformance.measuresEventGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNonePerformance.measuresEventGroup.event.patternVisualization.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNonePerformance.measuresEventTransitional.patternProcess.miningNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNonePerformance.measuresEventTransitional.patternCluster.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNonePerformance.measuresEventTransitional.patternVisualization.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNonePerformance.measuresTimeGroup.event.patternProcess.miningNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNonePerformance.measuresTimeGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNonePerformance.measuresTimeGroup.event.patternVisualization.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNonePerformance.measuresTimeTransitional.patternProcess.miningNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNonePerformance.measuresTimeTransitional.patternCluster.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNonePerformance.measuresTimeTransitional.patternVisualization.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLearner.characteristicsEventGroup.event.patternProcess.miningNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLearner.characteristicsEventGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLearner.characteristicsEventGroup.event.patternVisualization.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLearner.characteristicsEventTransitional.patternProcess.miningNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLearner.characteristicsEventTransitional.patternCluster.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLearner.characteristicsEventTransitional.patternVisualization.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLearner.characteristicsTimeGroup.event.patternProcess.miningNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLearner.characteristicsTimeGroup.event.patternCluster.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLearner.characteristicsTimeGroup.event.patternVisualization.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLearner.characteristicsTimeTransitional.patternProcess.miningNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLearner.characteristicsTimeTransitional.patternCluster.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
90Discovery and temporal analysis of MOOC study patternsClustering; EDM; LA; Learning Analytics; MOOCs; Markov model; Sequence mining; Study pattern; Temporal analysisWhat are the different study patterns of learners during MOOC assessment periods, and how do they evolve over time?Method.developmentNoneLearner.characteristicsTimeTransitional.patternVisualization.analysisNo.learning.focus.outcome2019Boroujeni, Mina Shirvani, Dillenbourg, Pierre
91Reliable Deep Grade Prediction with Uncertainty EstimationBayesian Deep Learning; Educational Data Mining; Grade Prediction; Sequential Models; UncertaintyNoneMethod.developmentNonePerformance.measuresEventOther.sequential.patternsNeural.networkNo.learning.focus.outcome2019Hu, Qian, Rangwala, Huzefa
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataEventEvent.sequenceFrequent.sequence.miningTime.on.learning2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataEventEvent.sequenceFrequent.sequence.miningCollaboration2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataEventEvent.sequenceProcess.miningTime.on.learning2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataEventEvent.sequenceProcess.miningCollaboration2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataEventTransitional.patternFrequent.sequence.miningTime.on.learning2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataEventTransitional.patternFrequent.sequence.miningCollaboration2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataEventTransitional.patternProcess.miningTime.on.learning2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataEventTransitional.patternProcess.miningCollaboration2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataTrace-otherEvent.sequenceFrequent.sequence.miningTime.on.learning2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataTrace-otherEvent.sequenceFrequent.sequence.miningCollaboration2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataTrace-otherEvent.sequenceProcess.miningTime.on.learning2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataTrace-otherEvent.sequenceProcess.miningCollaboration2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataTrace-otherTransitional.patternFrequent.sequence.miningTime.on.learning2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataTrace-otherTransitional.patternFrequent.sequence.miningCollaboration2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataTrace-otherTransitional.patternProcess.miningTime.on.learning2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Exploring.srl.processesSSRL; collaborative knowledge buildingLms.log.dataTrace-otherTransitional.patternProcess.miningCollaboration2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataEventEvent.sequenceFrequent.sequence.miningTime.on.learning2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataEventEvent.sequenceFrequent.sequence.miningCollaboration2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataEventEvent.sequenceProcess.miningTime.on.learning2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataEventEvent.sequenceProcess.miningCollaboration2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataEventTransitional.patternFrequent.sequence.miningTime.on.learning2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataEventTransitional.patternFrequent.sequence.miningCollaboration2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataEventTransitional.patternProcess.miningTime.on.learning2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataEventTransitional.patternProcess.miningCollaboration2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataTrace-otherEvent.sequenceFrequent.sequence.miningTime.on.learning2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataTrace-otherEvent.sequenceFrequent.sequence.miningCollaboration2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataTrace-otherEvent.sequenceProcess.miningTime.on.learning2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataTrace-otherEvent.sequenceProcess.miningCollaboration2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataTrace-otherTransitional.patternFrequent.sequence.miningTime.on.learning2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataTrace-otherTransitional.patternFrequent.sequence.miningCollaboration2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataTrace-otherTransitional.patternProcess.miningTime.on.learning2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
92Examining sequential patterns of self- and socially shared regulation of STEM learning in a CSCL environmentSTEM education; Self-regulated learning; Sequential mining; Socially shared regulation(1) How do self- and socially shared regulation activities occur in groups that successfully and less successfully complete group tasks? (2) Does the variation of regulatory activities differ between groups that successfully and less successfully complete the tasks? (3) Are there differential regulatory patterns associated with the performance of the groups in solving tasks?Group.comparisonSSRL; collaborative knowledge buildingLms.log.dataTrace-otherTransitional.patternProcess.miningCollaboration2019Zheng, Juan, Xing, Wanli, Zhu, Gaoxia
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataEventTransitional.patternProcess.miningLearning.indicators2019Cheng, Kun-Hung, Tsai, Chin-Chung
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataEventTransitional.patternProcess.miningCourse.design2019Cheng, Kun-Hung, Tsai, Chin-Chung
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataEventTransitional.patternBasic.statistical.analysisLearning.indicators2019Cheng, Kun-Hung, Tsai, Chin-Chung
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataEventTransitional.patternBasic.statistical.analysisCourse.design2019Cheng, Kun-Hung, Tsai, Chin-Chung
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataEventSummativeProcess.miningLearning.indicators2019Cheng, Kun-Hung, Tsai, Chin-Chung
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataEventSummativeProcess.miningCourse.design2019Cheng, Kun-Hung, Tsai, Chin-Chung
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataEventSummativeBasic.statistical.analysisLearning.indicators2019Cheng, Kun-Hung, Tsai, Chin-Chung
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataEventSummativeBasic.statistical.analysisCourse.design2019Cheng, Kun-Hung, Tsai, Chin-Chung
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataTimeTransitional.patternProcess.miningLearning.indicators2019Cheng, Kun-Hung, Tsai, Chin-Chung
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataTimeTransitional.patternProcess.miningCourse.design2019Cheng, Kun-Hung, Tsai, Chin-Chung
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataTimeTransitional.patternBasic.statistical.analysisLearning.indicators2019Cheng, Kun-Hung, Tsai, Chin-Chung
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataTimeTransitional.patternBasic.statistical.analysisCourse.design2019Cheng, Kun-Hung, Tsai, Chin-Chung
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataTimeSummativeProcess.miningLearning.indicators2019Cheng, Kun-Hung, Tsai, Chin-Chung
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataTimeSummativeProcess.miningCourse.design2019Cheng, Kun-Hung, Tsai, Chin-Chung
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataTimeSummativeBasic.statistical.analysisLearning.indicators2019Cheng, Kun-Hung, Tsai, Chin-Chung
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataTimeSummativeBasic.statistical.analysisCourse.design2019Cheng, Kun-Hung, Tsai, Chin-Chung
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataTrace-otherTransitional.patternProcess.miningLearning.indicators2019Cheng, Kun-Hung, Tsai, Chin-Chung
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataTrace-otherTransitional.patternProcess.miningCourse.design2019Cheng, Kun-Hung, Tsai, Chin-Chung
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataTrace-otherTransitional.patternBasic.statistical.analysisLearning.indicators2019Cheng, Kun-Hung, Tsai, Chin-Chung
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataTrace-otherTransitional.patternBasic.statistical.analysisCourse.design2019Cheng, Kun-Hung, Tsai, Chin-Chung
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataTrace-otherSummativeProcess.miningLearning.indicators2019Cheng, Kun-Hung, Tsai, Chin-Chung
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataTrace-otherSummativeProcess.miningCourse.design2019Cheng, Kun-Hung, Tsai, Chin-Chung
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataTrace-otherSummativeBasic.statistical.analysisLearning.indicators2019Cheng, Kun-Hung, Tsai, Chin-Chung
93A case study of immersive virtual field trips in an elementary classroom: Students’ learning experience and teacher-student interaction behaviorsElementary education; Improving classroom teaching; Interactive learning environments; Virtual realityWhat are elementary school students' perceptions of presence when engaging in educational immersive virtual field trips? 2. Do elementary school students' motivational beliefs change after learning by immersive virtual field trips? 3. What are the relationships between elementary school students' perceived presence, motivational beliefs, and attitudes toward immersive virtual field trips? 4. How does a teacher implement learning activities of immersive virtual field trips in a classroom? And how does the teacher interact with the students throughout the learning activities?Non-srl.indicators.identificationotherCustomized.log.dataTrace-otherSummativeBasic.statistical.analysisCourse.design2019Cheng, Kun-Hung, Tsai, Chin-Chung
94DEBE Feedback for Large Lecture Classroom AnalyticsLarge lectures; learning analytics; live feedback; mobile application; quantified selfNoneNon-srl.indicators.identificationaffective learningSelf-reportedEventSummativeBasic.statistical.analysisLearning.indicators2019Mitra, Ritayan, Chavan, Pankaj
94DEBE Feedback for Large Lecture Classroom AnalyticsLarge lectures; learning analytics; live feedback; mobile application; quantified selfNoneNon-srl.indicators.identificationaffective learningSelf-reportedEventSummativeBasic.statistical.analysisFeedback2019Mitra, Ritayan, Chavan, Pankaj
94DEBE Feedback for Large Lecture Classroom AnalyticsLarge lectures; learning analytics; live feedback; mobile application; quantified selfNoneNon-srl.indicators.identificationaffective learningSelf-reportedTimeSummativeBasic.statistical.analysisLearning.indicators2019Mitra, Ritayan, Chavan, Pankaj
94DEBE Feedback for Large Lecture Classroom AnalyticsLarge lectures; learning analytics; live feedback; mobile application; quantified selfNoneNon-srl.indicators.identificationaffective learningSelf-reportedTimeSummativeBasic.statistical.analysisFeedback2019Mitra, Ritayan, Chavan, Pankaj
94DEBE Feedback for Large Lecture Classroom AnalyticsLarge lectures; learning analytics; live feedback; mobile application; quantified selfNoneNon-srl.indicators.identificationaffective learningSelf-reportedTrace-feedbackSummativeBasic.statistical.analysisLearning.indicators2019Mitra, Ritayan, Chavan, Pankaj
94DEBE Feedback for Large Lecture Classroom AnalyticsLarge lectures; learning analytics; live feedback; mobile application; quantified selfNoneNon-srl.indicators.identificationaffective learningSelf-reportedTrace-feedbackSummativeBasic.statistical.analysisFeedback2019Mitra, Ritayan, Chavan, Pankaj
95Graph-Based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural NetworkEducational data mining; Graph neural Network analysis; Knowledge tracing; Learning sciencesNoneMethod.developmentknowledge tracingLms.log.dataEventOther.sequential.patternsNeural.networkNo.learning.focus.outcome2019Nakagawa, Hiromi, Iwasawa, Yusuke, Matsuo, Yutaka
95Graph-Based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural NetworkEducational data mining; Graph neural Network analysis; Knowledge tracing; Learning sciencesNoneMethod.developmentknowledge tracingLms.log.dataEventOther.sequential.patternsVisualization.analysisNo.learning.focus.outcome2019Nakagawa, Hiromi, Iwasawa, Yusuke, Matsuo, Yutaka
95Graph-Based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural NetworkEducational data mining; Graph neural Network analysis; Knowledge tracing; Learning sciencesNoneMethod.developmentknowledge tracingPerformance.measuresEventOther.sequential.patternsNeural.networkNo.learning.focus.outcome2019Nakagawa, Hiromi, Iwasawa, Yusuke, Matsuo, Yutaka
95Graph-Based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural NetworkEducational data mining; Graph neural Network analysis; Knowledge tracing; Learning sciencesNoneMethod.developmentknowledge tracingPerformance.measuresEventOther.sequential.patternsVisualization.analysisNo.learning.focus.outcome2019Nakagawa, Hiromi, Iwasawa, Yusuke, Matsuo, Yutaka
96Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised LearningNoneMethod.developmentNoneLms.log.dataEventOther.sequential.patternsNeural.networkNo.learning.focus.outcome2019Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised LearningNoneMethod.developmentNoneLms.log.dataEventOther.sequential.patternsVisualization.analysisNo.learning.focus.outcome2019Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised LearningNoneMethod.developmentNoneLms.log.dataTimeOther.sequential.patternsNeural.networkNo.learning.focus.outcome2019Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised LearningNoneMethod.developmentNoneLms.log.dataTimeOther.sequential.patternsVisualization.analysisNo.learning.focus.outcome2019Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised LearningNoneMethod.developmentNoneLms.log.dataTrace-videoOther.sequential.patternsNeural.networkNo.learning.focus.outcome2019Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised LearningNoneMethod.developmentNoneLms.log.dataTrace-videoOther.sequential.patternsVisualization.analysisNo.learning.focus.outcome2019Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised LearningNoneMethod.developmentNonePerformance.measuresEventOther.sequential.patternsNeural.networkNo.learning.focus.outcome2019Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised LearningNoneMethod.developmentNonePerformance.measuresEventOther.sequential.patternsVisualization.analysisNo.learning.focus.outcome2019Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised LearningNoneMethod.developmentNonePerformance.measuresTimeOther.sequential.patternsNeural.networkNo.learning.focus.outcome2019Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised LearningNoneMethod.developmentNonePerformance.measuresTimeOther.sequential.patternsVisualization.analysisNo.learning.focus.outcome2019Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised LearningNoneMethod.developmentNonePerformance.measuresTrace-videoOther.sequential.patternsNeural.networkNo.learning.focus.outcome2019Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
96Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online CoursesAutoencoder; Dimensionality Reduction; Feature Learning; Learning Behavior; Long Short-Term Memory; Unsupervised LearningNoneMethod.developmentNonePerformance.measuresTrace-videoOther.sequential.patternsVisualization.analysisNo.learning.focus.outcome2019Ding, Mucong, Yang, Kai, Yeung, Dit-Yan, Pong, Ting-Chuen
97Knowledge Tracing with Sequential Key-Value Memory Networksdeep learning; key-value memory; knowledge tracing; memory Network analysiss; sequence modellingIn this paper, we present a new KT model, called Sequential Key-Value Memory Networks (SKVMN). This model provides three advantages over the existing deep learning KT models.Method.developmentknowledge tracingLms.log.dataEventOther.sequential.patternsNeural.networkNo.learning.focus.outcome2019Abdelrahman, Ghodai, Wang, Qing
97Knowledge Tracing with Sequential Key-Value Memory Networksdeep learning; key-value memory; knowledge tracing; memory Network analysiss; sequence modellingIn this paper, we present a new KT model, called Sequential Key-Value Memory Networks (SKVMN). This model provides three advantages over the existing deep learning KT models.Method.developmentknowledge tracingLms.log.dataTrace-quizOther.sequential.patternsNeural.networkNo.learning.focus.outcome2019Abdelrahman, Ghodai, Wang, Qing
98Spatial-Temporal Data Association Based Ontology Alignment Research in High Education ContextData association; fuzzy ontology; fuzzy reasoning; ontology alignmentwe present a new method for ontology alignment based on the spatial-temporal data association and fuzzy ontology. By establishing fuzzy context ontology and formulating a series of fuzzy rules for fuzzy reasoning, the ontology alignment can be achieved.Method.developmentNoneMultimodalEventSummativeBasic.statistical.analysisNo.learning.focus.outcome2019Wang, Wei, Mu, Wenxin, Gou, Juanqiong
98Spatial-Temporal Data Association Based Ontology Alignment Research in High Education ContextData association; fuzzy ontology; fuzzy reasoning; ontology alignmentwe present a new method for ontology alignment based on the spatial-temporal data association and fuzzy ontology. By establishing fuzzy context ontology and formulating a series of fuzzy rules for fuzzy reasoning, the ontology alignment can be achieved.Method.developmentNoneMultimodalEventOther.sequential.patternsBasic.statistical.analysisNo.learning.focus.outcome2019Wang, Wei, Mu, Wenxin, Gou, Juanqiong
98Spatial-Temporal Data Association Based Ontology Alignment Research in High Education ContextData association; fuzzy ontology; fuzzy reasoning; ontology alignmentwe present a new method for ontology alignment based on the spatial-temporal data association and fuzzy ontology. By establishing fuzzy context ontology and formulating a series of fuzzy rules for fuzzy reasoning, the ontology alignment can be achieved.Method.developmentNoneMultimodalTimeSummativeBasic.statistical.analysisNo.learning.focus.outcome2019Wang, Wei, Mu, Wenxin, Gou, Juanqiong
98Spatial-Temporal Data Association Based Ontology Alignment Research in High Education ContextData association; fuzzy ontology; fuzzy reasoning; ontology alignmentwe present a new method for ontology alignment based on the spatial-temporal data association and fuzzy ontology. By establishing fuzzy context ontology and formulating a series of fuzzy rules for fuzzy reasoning, the ontology alignment can be achieved.Method.developmentNoneMultimodalTimeOther.sequential.patternsBasic.statistical.analysisNo.learning.focus.outcome2019Wang, Wei, Mu, Wenxin, Gou, Juanqiong
99Analysing the predictive power for anticipating assignment grades in a massive open online courseNoneNoneMethod.developmentNoneLms.log.dataEventSummativeOther.predictions.modelsNo.learning.focus.outcome2018Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99Analysing the predictive power for anticipating assignment grades in a massive open online courseNoneNoneMethod.developmentNoneLms.log.dataTimeSummativeOther.predictions.modelsNo.learning.focus.outcome2018Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99Analysing the predictive power for anticipating assignment grades in a massive open online courseNoneNoneMethod.developmentNoneLms.log.dataTrace-videoSummativeOther.predictions.modelsNo.learning.focus.outcome2018Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99Analysing the predictive power for anticipating assignment grades in a massive open online courseNoneNoneMethod.developmentNoneLms.log.dataTrace-quizSummativeOther.predictions.modelsNo.learning.focus.outcome2018Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99Analysing the predictive power for anticipating assignment grades in a massive open online courseNoneNoneMethod.developmentNonePerformance.measuresEventSummativeOther.predictions.modelsNo.learning.focus.outcome2018Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99Analysing the predictive power for anticipating assignment grades in a massive open online courseNoneNoneMethod.developmentNonePerformance.measuresTimeSummativeOther.predictions.modelsNo.learning.focus.outcome2018Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99Analysing the predictive power for anticipating assignment grades in a massive open online courseNoneNoneMethod.developmentNonePerformance.measuresTrace-videoSummativeOther.predictions.modelsNo.learning.focus.outcome2018Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99Analysing the predictive power for anticipating assignment grades in a massive open online courseNoneNoneMethod.developmentNonePerformance.measuresTrace-quizSummativeOther.predictions.modelsNo.learning.focus.outcome2018Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99Analysing the predictive power for anticipating assignment grades in a massive open online courseNoneNoneAt-risk.student.identificationNoneLms.log.dataEventSummativeOther.predictions.modelsNo.learning.focus.outcome2018Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99Analysing the predictive power for anticipating assignment grades in a massive open online courseNoneNoneAt-risk.student.identificationNoneLms.log.dataTimeSummativeOther.predictions.modelsNo.learning.focus.outcome2018Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99Analysing the predictive power for anticipating assignment grades in a massive open online courseNoneNoneAt-risk.student.identificationNoneLms.log.dataTrace-videoSummativeOther.predictions.modelsNo.learning.focus.outcome2018Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99Analysing the predictive power for anticipating assignment grades in a massive open online courseNoneNoneAt-risk.student.identificationNoneLms.log.dataTrace-quizSummativeOther.predictions.modelsNo.learning.focus.outcome2018Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99Analysing the predictive power for anticipating assignment grades in a massive open online courseNoneNoneAt-risk.student.identificationNonePerformance.measuresEventSummativeOther.predictions.modelsNo.learning.focus.outcome2018Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99Analysing the predictive power for anticipating assignment grades in a massive open online courseNoneNoneAt-risk.student.identificationNonePerformance.measuresTimeSummativeOther.predictions.modelsNo.learning.focus.outcome2018Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99Analysing the predictive power for anticipating assignment grades in a massive open online courseNoneNoneAt-risk.student.identificationNonePerformance.measuresTrace-videoSummativeOther.predictions.modelsNo.learning.focus.outcome2018Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
99Analysing the predictive power for anticipating assignment grades in a massive open online courseNoneNoneAt-risk.student.identificationNonePerformance.measuresTrace-quizSummativeOther.predictions.modelsNo.learning.focus.outcome2018Moreno-Marcos, Pedro Manuel, Mu{\~n}oz-Merino, Pedro J, Alario-Hoyos, Carlos, Estevez-Ayres, Iria, {Delgado Kloos}, Carlos
100Investigating temporal access in a flipped classroom: procrastination persistsBehavorial Science; Education; Education & Educational Research; Information Systems Applications (incl.Internet); Public Policy; Soci; and LawWhat temporal patterns of behavior may be discernable among university students in a mobile applications computer science course?To what extent might any such patterns of behavior relate to student performance?  Are there any significant differences among student performance groups with respect to LMS interaction?Group.comparisonNoneLms.log.dataEventSummativeBasic.statistical.analysisTime.on.learning2018AlJarrah, Abeer, Thomas, Michael K, Shehab, Mohamed
100Investigating temporal access in a flipped classroom: procrastination persistsBehavorial Science; Education; Education & Educational Research; Information Systems Applications (incl.Internet); Public Policy; Soci; and LawWhat temporal patterns of behavior may be discernable among university students in a mobile applications computer science course?To what extent might any such patterns of behavior relate to student performance?  Are there any significant differences among student performance groups with respect to LMS interaction?Group.comparisonNoneLms.log.dataTimeSummativeBasic.statistical.analysisTime.on.learning2018AlJarrah, Abeer, Thomas, Michael K, Shehab, Mohamed
100Investigating temporal access in a flipped classroom: procrastination persistsBehavorial Science; Education; Education & Educational Research; Information Systems Applications (incl.Internet); Public Policy; Soci; and LawWhat temporal patterns of behavior may be discernable among university students in a mobile applications computer science course?To what extent might any such patterns of behavior relate to student performance?  Are there any significant differences among student performance groups with respect to LMS interaction?Group.comparisonNonePerformance.measuresEventSummativeBasic.statistical.analysisTime.on.learning2018AlJarrah, Abeer, Thomas, Michael K, Shehab, Mohamed
100Investigating temporal access in a flipped classroom: procrastination persistsBehavorial Science; Education; Education & Educational Research; Information Systems Applications (incl.Internet); Public Policy; Soci; and LawWhat temporal patterns of behavior may be discernable among university students in a mobile applications computer science course?To what extent might any such patterns of behavior relate to student performance?  Are there any significant differences among student performance groups with respect to LMS interaction?Group.comparisonNonePerformance.measuresTimeSummativeBasic.statistical.analysisTime.on.learning2018AlJarrah, Abeer, Thomas, Michael K, Shehab, Mohamed
101What’s Next? A Recommendation System for Industrial TrainingChemistry and Earth Sciences; Computer Science; Industrial trainin; Physics; Statistics for EngineeringNoneMethod.developmentNoneLearner.characteristicsEventOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2018Srivastava, Rajiv, Palshikar, Girish Keshav, Chaurasia, Saheb, Dixit, Arati
102Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learningefficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence miningNoneExploring.srl.processessrl; affective learning; game-based learningCustomized.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2018Taub, Michelle, Azevedo, Roger
102Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learningefficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence miningNoneExploring.srl.processessrl; affective learning; game-based learningCustomized.log.dataTrace-otherEvent.sequenceFrequent.sequence.miningLearning.indicators2018Taub, Michelle, Azevedo, Roger
102Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learningefficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence miningNoneExploring.srl.processessrl; affective learning; game-based learningSelf-reportedEventEvent.sequenceFrequent.sequence.miningLearning.indicators2018Taub, Michelle, Azevedo, Roger
102Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learningefficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence miningNoneExploring.srl.processessrl; affective learning; game-based learningSelf-reportedTrace-otherEvent.sequenceFrequent.sequence.miningLearning.indicators2018Taub, Michelle, Azevedo, Roger
102Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learningefficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence miningNoneExploring.srl.processessrl; affective learning; game-based learningMultimodalEventEvent.sequenceFrequent.sequence.miningLearning.indicators2018Taub, Michelle, Azevedo, Roger
102Using Sequence Mining to Analyze Metacognitive Monitoring and Scientific Inquiry based on Levels of Efficiency and Emotions during Game-Based Learningefficiency; emotions; game-based learning; scientific inquiry; self-regulated learning; sequence miningNoneExploring.srl.processessrl; affective learning; game-based learningMultimodalTrace-otherEvent.sequenceFrequent.sequence.miningLearning.indicators2018Taub, Michelle, Azevedo, Roger
103A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporalitycollaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socioNoneExploring.socio-dynamicscollaborative knowledge buildingCustomized.log.dataEventOther.sequential.patternsNetwork.analysisLearning.indicators2018Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporalitycollaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socioNoneExploring.socio-dynamicscollaborative knowledge buildingCustomized.log.dataEventOther.sequential.patternsContent.analysisLearning.indicators2018Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporalitycollaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socioNoneExploring.socio-dynamicscollaborative knowledge buildingCustomized.log.dataEventOther.sequential.patternsVisualization.analysisLearning.indicators2018Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporalitycollaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socioNoneExploring.socio-dynamicscollaborative knowledge buildingCustomized.log.dataTrace-forumOther.sequential.patternsNetwork.analysisLearning.indicators2018Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporalitycollaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socioNoneExploring.socio-dynamicscollaborative knowledge buildingCustomized.log.dataTrace-forumOther.sequential.patternsContent.analysisLearning.indicators2018Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporalitycollaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socioNoneExploring.socio-dynamicscollaborative knowledge buildingCustomized.log.dataTrace-forumOther.sequential.patternsVisualization.analysisLearning.indicators2018Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporalitycollaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socioNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventOther.sequential.patternsNetwork.analysisLearning.indicators2018Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporalitycollaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socioNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventOther.sequential.patternsContent.analysisLearning.indicators2018Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporalitycollaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socioNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventOther.sequential.patternsVisualization.analysisLearning.indicators2018Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporalitycollaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socioNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumOther.sequential.patternsNetwork.analysisLearning.indicators2018Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporalitycollaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socioNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumOther.sequential.patternsContent.analysisLearning.indicators2018Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
103A Mixed-Methods Approach to Analyze Shared Epistemic Agency in Jigsaw Instruction at Multiple Scales of Temporalitycollaborative learning; dialogical discourse analysis; jigsaw instruction.; methods research; semantic Network analysis analysis; shared epistemic agency; socioNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumOther.sequential.patternsVisualization.analysisLearning.indicators2018Oshima, Jun, Oshima, Ritsuko, Fujita, Wataru
104A Sequence Data Model for Analyzing Temporal Patterns of Student DataSequence data model; educational data mining; knowledge discovery; learning analytics; predictive modellingNoneMethod.developmentNoneLms.log.dataEventOther.sequential.patternsCluster.analysisNo.learning.focus.outcome2018Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon
104A Sequence Data Model for Analyzing Temporal Patterns of Student DataSequence data model; educational data mining; knowledge discovery; learning analytics; predictive modellingNoneMethod.developmentNoneLms.log.dataTimeOther.sequential.patternsCluster.analysisNo.learning.focus.outcome2018Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon
104A Sequence Data Model for Analyzing Temporal Patterns of Student DataSequence data model; educational data mining; knowledge discovery; learning analytics; predictive modellingNoneMethod.developmentNonePerformance.measuresEventOther.sequential.patternsCluster.analysisNo.learning.focus.outcome2018Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon
104A Sequence Data Model for Analyzing Temporal Patterns of Student DataSequence data model; educational data mining; knowledge discovery; learning analytics; predictive modellingNoneMethod.developmentNonePerformance.measuresTimeOther.sequential.patternsCluster.analysisNo.learning.focus.outcome2018Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon
104A Sequence Data Model for Analyzing Temporal Patterns of Student DataSequence data model; educational data mining; knowledge discovery; learning analytics; predictive modellingNoneMethod.developmentNoneLearner.characteristicsEventOther.sequential.patternsCluster.analysisNo.learning.focus.outcome2018Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon
104A Sequence Data Model for Analyzing Temporal Patterns of Student DataSequence data model; educational data mining; knowledge discovery; learning analytics; predictive modellingNoneMethod.developmentNoneLearner.characteristicsTimeOther.sequential.patternsCluster.analysisNo.learning.focus.outcome2018Mahzoon, Mohammad Javad, Maher, Mary Lou, Eltayeby, Omar, Dou, Wenwen, Grace, Kazjon
105Recurrence Quantification Analysis as a Method for Studying Text Comprehension Dynamicsdynamical systems theory; reading; recurrence quantification analysis; self-explanation; text comprehensionNoneMethod.developmentotherLearning.productTrace-readingSummativeNeural.networkLearning.indicators2018Likens, Aaron D, McCarthy, Kathryn S, Allen, Laura K, McNamara, Danielle S
105Recurrence Quantification Analysis as a Method for Studying Text Comprehension Dynamicsdynamical systems theory; reading; recurrence quantification analysis; self-explanation; text comprehensionNoneMethod.developmentotherLearning.productTrace-readingSummativeContent.analysisLearning.indicators2018Likens, Aaron D, McCarthy, Kathryn S, Allen, Laura K, McNamara, Danielle S
105Recurrence Quantification Analysis as a Method for Studying Text Comprehension Dynamicsdynamical systems theory; reading; recurrence quantification analysis; self-explanation; text comprehensionNoneMethod.developmentotherLearning.productTrace-quizSummativeNeural.networkLearning.indicators2018Likens, Aaron D, McCarthy, Kathryn S, Allen, Laura K, McNamara, Danielle S
105Recurrence Quantification Analysis as a Method for Studying Text Comprehension Dynamicsdynamical systems theory; reading; recurrence quantification analysis; self-explanation; text comprehensionNoneMethod.developmentotherLearning.productTrace-quizSummativeContent.analysisLearning.indicators2018Likens, Aaron D, McCarthy, Kathryn S, Allen, Laura K, McNamara, Danielle S
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationCustomized.log.dataEventSummativeProcess.miningLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationCustomized.log.dataEventSummativeBasic.statistical.analysisLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationCustomized.log.dataEventTransitional.patternProcess.miningLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationCustomized.log.dataEventTransitional.patternBasic.statistical.analysisLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationCustomized.log.dataTrace-readingSummativeProcess.miningLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationCustomized.log.dataTrace-readingSummativeBasic.statistical.analysisLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationCustomized.log.dataTrace-readingTransitional.patternProcess.miningLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationCustomized.log.dataTrace-readingTransitional.patternBasic.statistical.analysisLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationCustomized.log.dataTrace-quizSummativeProcess.miningLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationCustomized.log.dataTrace-quizSummativeBasic.statistical.analysisLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationCustomized.log.dataTrace-quizTransitional.patternProcess.miningLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationCustomized.log.dataTrace-quizTransitional.patternBasic.statistical.analysisLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationSelf-reportedEventSummativeProcess.miningLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationSelf-reportedEventSummativeBasic.statistical.analysisLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationSelf-reportedEventTransitional.patternProcess.miningLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationSelf-reportedEventTransitional.patternBasic.statistical.analysisLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationSelf-reportedTrace-readingSummativeProcess.miningLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationSelf-reportedTrace-readingSummativeBasic.statistical.analysisLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationSelf-reportedTrace-readingTransitional.patternProcess.miningLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationSelf-reportedTrace-readingTransitional.patternBasic.statistical.analysisLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationSelf-reportedTrace-quizSummativeProcess.miningLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationSelf-reportedTrace-quizSummativeBasic.statistical.analysisLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationSelf-reportedTrace-quizTransitional.patternProcess.miningLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
106Applying learning analytics to explore the effects of motivation on online students' reading behavioral patternsBehavioral pattern; Learning analytics; Motivation; Online learning; Sequential analysisNoneNon-srl.indicators.identificationmotivationSelf-reportedTrace-quizTransitional.patternBasic.statistical.analysisLearning.indicators2018Sun, Jerry Chih Yuan, Lin, Che Tsun, Chou, Chien
107Understanding user behavioral patterns in open knowledge communitiesOpen knowledge community; behavioral pattern; knowledge sharing; sequential analysisNoneNon-srl.indicators.identificationcollaborative knowledge buildingCustomized.log.dataEventTransitional.patternProcess.miningLearning.indicators2018Yang, Xianmin, Song, Shuqiang, Zhao, Xinshuo, Yu, Shengquan
107Understanding user behavioral patterns in open knowledge communitiesOpen knowledge community; behavioral pattern; knowledge sharing; sequential analysisNoneNon-srl.indicators.identificationcollaborative knowledge buildingCustomized.log.dataTrace-readingTransitional.patternProcess.miningLearning.indicators2018Yang, Xianmin, Song, Shuqiang, Zhao, Xinshuo, Yu, Shengquan
107Understanding user behavioral patterns in open knowledge communitiesOpen knowledge community; behavioral pattern; knowledge sharing; sequential analysisNoneNon-srl.indicators.identificationcollaborative knowledge buildingCustomized.log.dataTrace-forumTransitional.patternProcess.miningLearning.indicators2018Yang, Xianmin, Song, Shuqiang, Zhao, Xinshuo, Yu, Shengquan
107Understanding user behavioral patterns in open knowledge communitiesOpen knowledge community; behavioral pattern; knowledge sharing; sequential analysisNoneNon-srl.indicators.identificationcollaborative knowledge buildingCustomized.log.dataTrace-otherTransitional.patternProcess.miningLearning.indicators2018Yang, Xianmin, Song, Shuqiang, Zhao, Xinshuo, Yu, Shengquan
108Predicting Learning Difficulty Based on Gaze and Pupil Responsee-learning; eye movement analysis; eye tracking; predicting learning difficulty; predicting levels of learning; pupillary response analysisNoneMethod.developmentotherMultimodalTimeNoneOther.predictions.modelsTime.on.learning2018Parikh, Saurin, Kalva, Hari
108Predicting Learning Difficulty Based on Gaze and Pupil Responsee-learning; eye movement analysis; eye tracking; predicting learning difficulty; predicting levels of learning; pupillary response analysisNoneMethod.developmentotherMultimodalTrace-readingNoneOther.predictions.modelsTime.on.learning2018Parikh, Saurin, Kalva, Hari
109Social tagging strategy for enhancing e-learning experienceArchitectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategiesNoneMethod.developmentcollaborative knowledge buildingCustomized.log.dataTrace-readingOther.sequential.patternsOther.predictions.modelsCourse.design2018Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
109Social tagging strategy for enhancing e-learning experienceArchitectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategiesNoneMethod.developmentcollaborative knowledge buildingCustomized.log.dataTrace-readingOther.sequential.patternsOther.predictions.modelsFeedback2018Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
109Social tagging strategy for enhancing e-learning experienceArchitectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategiesNoneMethod.developmentcollaborative knowledge buildingCustomized.log.dataTrace-otherOther.sequential.patternsOther.predictions.modelsCourse.design2018Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
109Social tagging strategy for enhancing e-learning experienceArchitectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategiesNoneMethod.developmentcollaborative knowledge buildingCustomized.log.dataTrace-otherOther.sequential.patternsOther.predictions.modelsFeedback2018Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
109Social tagging strategy for enhancing e-learning experienceArchitectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategiesNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-readingOther.sequential.patternsOther.predictions.modelsCourse.design2018Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
109Social tagging strategy for enhancing e-learning experienceArchitectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategiesNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-readingOther.sequential.patternsOther.predictions.modelsFeedback2018Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
109Social tagging strategy for enhancing e-learning experienceArchitectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategiesNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-otherOther.sequential.patternsOther.predictions.modelsCourse.design2018Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
109Social tagging strategy for enhancing e-learning experienceArchitectures for educational technology system; Human-computer interface; Intelligent tutoring systems; Programming and programming languages; Teaching/learning strategiesNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-otherOther.sequential.patternsOther.predictions.modelsFeedback2018Klavsnja-Milicevic, Aleksandra, Vesin, Boban, Ivanovic, Mirjana
110How FLOSS Participation Supports Lifelong Learning and Working: Apprenticeship Across Time and SpatialitiesFLOSS; learning across scales; situated cognitionNoneMethod.developmentcollaborative knowledge buildingContextualTimeNoneQualitative.analysisCourse.design2018Johri, Aditya
110How FLOSS Participation Supports Lifelong Learning and Working: Apprenticeship Across Time and SpatialitiesFLOSS; learning across scales; situated cognitionNoneMethod.developmentcollaborative knowledge buildingContextualTimeNoneQualitative.analysisTime.on.learning2018Johri, Aditya
111Timing Matters: Approaches for Measuring and Visualizing Behaviours of Timing and Spacing of Work in Self-Paced Online Teacher Professional Development CoursesTiming; participation; engagement; repetition; online learning; distance education; informal learning; self- paced learning; professional development; procrastination; spacing effectNoneNon-srl.indicators.identificationotherLms.log.dataTimeSummativeBasic.statistical.analysisTime.on.learning2018Riel, Jeremy, Lawless, Kimberly A., Brown, Scott W.
111Timing Matters: Approaches for Measuring and Visualizing Behaviours of Timing and Spacing of Work in Self-Paced Online Teacher Professional Development CoursesTiming; participation; engagement; repetition; online learning; distance education; informal learning; self- paced learning; professional development; procrastination; spacing effectNoneNon-srl.indicators.identificationotherLms.log.dataTimeSummativeVisualization.analysisTime.on.learning2018Riel, Jeremy, Lawless, Kimberly A., Brown, Scott W.
112Observational Scaffolding for Learning Analytics: A Methodological ProposalLag sequential analysis; Learning Analytics; Observational methodology; Polar coordinate analysis; Temporal analyticsNoneMethod.developmentcollaborative knowledge buildingCustomized.log.dataTrace-forumTransitional.patternProcess.miningCollaboration2018Rodriguez-Medina, Jairo, Rodriguez-Triana, Maria Jesus, Eradze, Maka, Garcia-Sastre, Sara
112Observational Scaffolding for Learning Analytics: A Methodological ProposalLag sequential analysis; Learning Analytics; Observational methodology; Polar coordinate analysis; Temporal analyticsNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-forumTransitional.patternProcess.miningCollaboration2018Rodriguez-Medina, Jairo, Rodriguez-Triana, Maria Jesus, Eradze, Maka, Garcia-Sastre, Sara
113Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ BehavioursLearning analytics; engagement; learning design; temporal analysis; time managementNoneNon-srl.indicators.identificationtime managementLms.log.dataEventSummativeBasic.statistical.analysisCourse.design2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
113Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ BehavioursLearning analytics; engagement; learning design; temporal analysis; time managementNoneNon-srl.indicators.identificationtime managementLms.log.dataEventSummativeVisualization.analysisCourse.design2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
113Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ BehavioursLearning analytics; engagement; learning design; temporal analysis; time managementNoneNon-srl.indicators.identificationtime managementLms.log.dataTimeSummativeBasic.statistical.analysisCourse.design2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
113Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ BehavioursLearning analytics; engagement; learning design; temporal analysis; time managementNoneNon-srl.indicators.identificationtime managementLms.log.dataTimeSummativeVisualization.analysisCourse.design2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
113Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ BehavioursLearning analytics; engagement; learning design; temporal analysis; time managementNoneNon-srl.indicators.identificationtime managementLms.log.dataTrace-readingSummativeBasic.statistical.analysisCourse.design2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
113Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ BehavioursLearning analytics; engagement; learning design; temporal analysis; time managementNoneNon-srl.indicators.identificationtime managementLms.log.dataTrace-readingSummativeVisualization.analysisCourse.design2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
113Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ BehavioursLearning analytics; engagement; learning design; temporal analysis; time managementNoneNon-srl.indicators.identificationtime managementLms.log.dataTrace-forumSummativeBasic.statistical.analysisCourse.design2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
113Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ BehavioursLearning analytics; engagement; learning design; temporal analysis; time managementNoneNon-srl.indicators.identificationtime managementLms.log.dataTrace-forumSummativeVisualization.analysisCourse.design2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
113Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ BehavioursLearning analytics; engagement; learning design; temporal analysis; time managementNoneNon-srl.indicators.identificationtime managementLms.log.dataTrace-otherSummativeBasic.statistical.analysisCourse.design2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
113Using Temporal Analytics to Detect Inconsistencies Between Learning Design and Students’ BehavioursLearning analytics; engagement; learning design; temporal analysis; time managementNoneNon-srl.indicators.identificationtime managementLms.log.dataTrace-otherSummativeVisualization.analysisCourse.design2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
114Applying Learning Analytics for the Early Prediction of Students' Academic Performance in Blended LearningAcademic achievement; Academic learning; Analysis; Analytics; Big data; Blended learning; Data management; Datasets; Distance learning; Educational aspects; Educational environment; Internet resources; Learning; Massive open online courses; Mathematical analysis; Online learning; Performance prediction; Real variables; Regression analysis; Special Issue Articles; StudentsNoneAt-risk.student.identificationNoneLms.log.dataEventSummativeOther.predictions.modelsNo.learning.focus.outcome2018Lu, Owen H T, Huang, Anna Y Q, Huang, Jeff C H, Lin, Albert J Q, Ogata, Hiroaki, Yang, Stephen J H
114Applying Learning Analytics for the Early Prediction of Students' Academic Performance in Blended LearningAcademic achievement; Academic learning; Analysis; Analytics; Big data; Blended learning; Data management; Datasets; Distance learning; Educational aspects; Educational environment; Internet resources; Learning; Massive open online courses; Mathematical analysis; Online learning; Performance prediction; Real variables; Regression analysis; Special Issue Articles; StudentsNoneAt-risk.student.identificationNonePerformance.measuresEventSummativeOther.predictions.modelsNo.learning.focus.outcome2018Lu, Owen H T, Huang, Anna Y Q, Huang, Jeff C H, Lin, Albert J Q, Ogata, Hiroaki, Yang, Stephen J H
115Effects of success v failure cases on learner-learner interactionCase-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learningNoneNon-srl.indicators.identificationotherLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2018Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115Effects of success v failure cases on learner-learner interactionCase-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learningNoneNon-srl.indicators.identificationotherLms.log.dataEventTransitional.patternContent.analysisLearning.indicators2018Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115Effects of success v failure cases on learner-learner interactionCase-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learningNoneNon-srl.indicators.identificationotherLms.log.dataEventSummativeProcess.miningLearning.indicators2018Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115Effects of success v failure cases on learner-learner interactionCase-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learningNoneNon-srl.indicators.identificationotherLms.log.dataEventSummativeContent.analysisLearning.indicators2018Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115Effects of success v failure cases on learner-learner interactionCase-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learningNoneNon-srl.indicators.identificationotherLms.log.dataTrace-forumTransitional.patternProcess.miningLearning.indicators2018Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115Effects of success v failure cases on learner-learner interactionCase-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learningNoneNon-srl.indicators.identificationotherLms.log.dataTrace-forumTransitional.patternContent.analysisLearning.indicators2018Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115Effects of success v failure cases on learner-learner interactionCase-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learningNoneNon-srl.indicators.identificationotherLms.log.dataTrace-forumSummativeProcess.miningLearning.indicators2018Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115Effects of success v failure cases on learner-learner interactionCase-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learningNoneNon-srl.indicators.identificationotherLms.log.dataTrace-forumSummativeContent.analysisLearning.indicators2018Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115Effects of success v failure cases on learner-learner interactionCase-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learningNoneGroup.comparisonotherLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2018Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115Effects of success v failure cases on learner-learner interactionCase-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learningNoneGroup.comparisonotherLms.log.dataEventTransitional.patternContent.analysisLearning.indicators2018Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115Effects of success v failure cases on learner-learner interactionCase-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learningNoneGroup.comparisonotherLms.log.dataEventSummativeProcess.miningLearning.indicators2018Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115Effects of success v failure cases on learner-learner interactionCase-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learningNoneGroup.comparisonotherLms.log.dataEventSummativeContent.analysisLearning.indicators2018Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115Effects of success v failure cases on learner-learner interactionCase-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learningNoneGroup.comparisonotherLms.log.dataTrace-forumTransitional.patternProcess.miningLearning.indicators2018Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115Effects of success v failure cases on learner-learner interactionCase-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learningNoneGroup.comparisonotherLms.log.dataTrace-forumTransitional.patternContent.analysisLearning.indicators2018Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115Effects of success v failure cases on learner-learner interactionCase-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learningNoneGroup.comparisonotherLms.log.dataTrace-forumSummativeProcess.miningLearning.indicators2018Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
115Effects of success v failure cases on learner-learner interactionCase-based reasoning; Contrasting cases; Failure-driven memory theory; Online learning; Problem-based learningNoneGroup.comparisonotherLms.log.dataTrace-forumSummativeContent.analysisLearning.indicators2018Tawfik, Andrew A, Giabbanelli, Philippe J, Hogan, Maureen, Msilu, Fortunata, Gill, Anila, York, Cindy S
116Linking Students' Timing of Engagement to Learning Design and Academic Performanceengagement; higher education; learning analytics; learning design; temporal; virtual learning environmentNoneNon-srl.indicators.identificationtime on taskLms.log.dataEventSummativeBasic.statistical.analysisCourse.design2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
116Linking Students' Timing of Engagement to Learning Design and Academic Performanceengagement; higher education; learning analytics; learning design; temporal; virtual learning environmentNoneNon-srl.indicators.identificationtime on taskLms.log.dataEventSummativeBasic.statistical.analysisTime.on.learning2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
116Linking Students' Timing of Engagement to Learning Design and Academic Performanceengagement; higher education; learning analytics; learning design; temporal; virtual learning environmentNoneNon-srl.indicators.identificationtime on taskLms.log.dataEventSummativeVisualization.analysisCourse.design2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
116Linking Students' Timing of Engagement to Learning Design and Academic Performanceengagement; higher education; learning analytics; learning design; temporal; virtual learning environmentNoneNon-srl.indicators.identificationtime on taskLms.log.dataEventSummativeVisualization.analysisTime.on.learning2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
116Linking Students' Timing of Engagement to Learning Design and Academic Performanceengagement; higher education; learning analytics; learning design; temporal; virtual learning environmentNoneNon-srl.indicators.identificationtime on taskLms.log.dataTimeSummativeBasic.statistical.analysisCourse.design2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
116Linking Students' Timing of Engagement to Learning Design and Academic Performanceengagement; higher education; learning analytics; learning design; temporal; virtual learning environmentNoneNon-srl.indicators.identificationtime on taskLms.log.dataTimeSummativeBasic.statistical.analysisTime.on.learning2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
116Linking Students' Timing of Engagement to Learning Design and Academic Performanceengagement; higher education; learning analytics; learning design; temporal; virtual learning environmentNoneNon-srl.indicators.identificationtime on taskLms.log.dataTimeSummativeVisualization.analysisCourse.design2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
116Linking Students' Timing of Engagement to Learning Design and Academic Performanceengagement; higher education; learning analytics; learning design; temporal; virtual learning environmentNoneNon-srl.indicators.identificationtime on taskLms.log.dataTimeSummativeVisualization.analysisTime.on.learning2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
116Linking Students' Timing of Engagement to Learning Design and Academic Performanceengagement; higher education; learning analytics; learning design; temporal; virtual learning environmentNoneGroup.comparisontime on taskLms.log.dataEventSummativeBasic.statistical.analysisCourse.design2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
116Linking Students' Timing of Engagement to Learning Design and Academic Performanceengagement; higher education; learning analytics; learning design; temporal; virtual learning environmentNoneGroup.comparisontime on taskLms.log.dataEventSummativeBasic.statistical.analysisTime.on.learning2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
116Linking Students' Timing of Engagement to Learning Design and Academic Performanceengagement; higher education; learning analytics; learning design; temporal; virtual learning environmentNoneGroup.comparisontime on taskLms.log.dataEventSummativeVisualization.analysisCourse.design2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
116Linking Students' Timing of Engagement to Learning Design and Academic Performanceengagement; higher education; learning analytics; learning design; temporal; virtual learning environmentNoneGroup.comparisontime on taskLms.log.dataEventSummativeVisualization.analysisTime.on.learning2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
116Linking Students' Timing of Engagement to Learning Design and Academic Performanceengagement; higher education; learning analytics; learning design; temporal; virtual learning environmentNoneGroup.comparisontime on taskLms.log.dataTimeSummativeBasic.statistical.analysisCourse.design2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
116Linking Students' Timing of Engagement to Learning Design and Academic Performanceengagement; higher education; learning analytics; learning design; temporal; virtual learning environmentNoneGroup.comparisontime on taskLms.log.dataTimeSummativeBasic.statistical.analysisTime.on.learning2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
116Linking Students' Timing of Engagement to Learning Design and Academic Performanceengagement; higher education; learning analytics; learning design; temporal; virtual learning environmentNoneGroup.comparisontime on taskLms.log.dataTimeSummativeVisualization.analysisCourse.design2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
116Linking Students' Timing of Engagement to Learning Design and Academic Performanceengagement; higher education; learning analytics; learning design; temporal; virtual learning environmentNoneGroup.comparisontime on taskLms.log.dataTimeSummativeVisualization.analysisTime.on.learning2018Nguyen, Quan, Huptych, Michal, Rienties, Bart
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneGroup.comparisonNoneLms.log.dataEventTransitional.patternProcess.miningNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneGroup.comparisonNoneLms.log.dataEventTransitional.patternCluster.analysisNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneGroup.comparisonNoneLms.log.dataEventTransitional.patternVisualization.analysisNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneGroup.comparisonNoneLms.log.dataEventSummativeProcess.miningNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneGroup.comparisonNoneLms.log.dataEventSummativeCluster.analysisNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneGroup.comparisonNoneLms.log.dataEventSummativeVisualization.analysisNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneGroup.comparisonNoneLms.log.dataTimeTransitional.patternProcess.miningNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneGroup.comparisonNoneLms.log.dataTimeTransitional.patternCluster.analysisNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneGroup.comparisonNoneLms.log.dataTimeTransitional.patternVisualization.analysisNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneGroup.comparisonNoneLms.log.dataTimeSummativeProcess.miningNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneGroup.comparisonNoneLms.log.dataTimeSummativeCluster.analysisNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneGroup.comparisonNoneLms.log.dataTimeSummativeVisualization.analysisNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneAt-risk.student.identificationNoneLms.log.dataEventTransitional.patternProcess.miningNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneAt-risk.student.identificationNoneLms.log.dataEventTransitional.patternCluster.analysisNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneAt-risk.student.identificationNoneLms.log.dataEventTransitional.patternVisualization.analysisNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneAt-risk.student.identificationNoneLms.log.dataEventSummativeProcess.miningNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneAt-risk.student.identificationNoneLms.log.dataEventSummativeCluster.analysisNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneAt-risk.student.identificationNoneLms.log.dataEventSummativeVisualization.analysisNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneAt-risk.student.identificationNoneLms.log.dataTimeTransitional.patternProcess.miningNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneAt-risk.student.identificationNoneLms.log.dataTimeTransitional.patternCluster.analysisNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneAt-risk.student.identificationNoneLms.log.dataTimeTransitional.patternVisualization.analysisNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneAt-risk.student.identificationNoneLms.log.dataTimeSummativeProcess.miningNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneAt-risk.student.identificationNoneLms.log.dataTimeSummativeCluster.analysisNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
117Temporal Dynamics of MOOC Learning TrajectoriesMOOCs; behavioral analysis; educational process mining; temporal modelling; process miningNoneAt-risk.student.identificationNoneLms.log.dataTimeSummativeVisualization.analysisNo.learning.focus.outcome2018Rizvi, Saman, Rienties, Bart, Rogaten, Jekaterina
118A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring SystemsLearning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learningNoneMethod.developmentNoneCustomized.log.dataEventNoneBasic.statistical.analysisCourse.design2018Liu, Ran, Stamper, John C, Davenport, Jodi
118A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring SystemsLearning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learningNoneMethod.developmentNoneCustomized.log.dataTrace-videoNoneBasic.statistical.analysisCourse.design2018Liu, Ran, Stamper, John C, Davenport, Jodi
118A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring SystemsLearning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learningNoneMethod.developmentNoneCustomized.log.dataTrace-otherNoneBasic.statistical.analysisCourse.design2018Liu, Ran, Stamper, John C, Davenport, Jodi
118A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring SystemsLearning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learningNoneMethod.developmentNoneMultimodalEventNoneBasic.statistical.analysisCourse.design2018Liu, Ran, Stamper, John C, Davenport, Jodi
118A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring SystemsLearning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learningNoneMethod.developmentNoneMultimodalTrace-videoNoneBasic.statistical.analysisCourse.design2018Liu, Ran, Stamper, John C, Davenport, Jodi
118A Novel Method for the In-Depth Multimodal Analysis of Student Learning Trajectories in Intelligent Tutoring SystemsLearning trajectories; intelligent tutoring systems; learning curves; mixed methods; multimodal data; science learningNoneMethod.developmentNoneMultimodalTrace-otherNoneBasic.statistical.analysisCourse.design2018Liu, Ran, Stamper, John C, Davenport, Jodi
119Behavioral patterns of knowledge construction in online cooperative translation activitiesBehavioral pattern; Cooperative translation; Engagement; Knowledge constructionNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataEventTransitional.patternFrequent.sequence.miningCollaboration2018Yang, Xianmin, Li, Jihong, Xing, Beibei
119Behavioral patterns of knowledge construction in online cooperative translation activitiesBehavioral pattern; Cooperative translation; Engagement; Knowledge constructionNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataEventTransitional.patternProcess.miningCollaboration2018Yang, Xianmin, Li, Jihong, Xing, Beibei
119Behavioral patterns of knowledge construction in online cooperative translation activitiesBehavioral pattern; Cooperative translation; Engagement; Knowledge constructionNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternFrequent.sequence.miningCollaboration2018Yang, Xianmin, Li, Jihong, Xing, Beibei
119Behavioral patterns of knowledge construction in online cooperative translation activitiesBehavioral pattern; Cooperative translation; Engagement; Knowledge constructionNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternProcess.miningCollaboration2018Yang, Xianmin, Li, Jihong, Xing, Beibei
119Behavioral patterns of knowledge construction in online cooperative translation activitiesBehavioral pattern; Cooperative translation; Engagement; Knowledge constructionNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTrace-otherTransitional.patternFrequent.sequence.miningCollaboration2018Yang, Xianmin, Li, Jihong, Xing, Beibei
119Behavioral patterns of knowledge construction in online cooperative translation activitiesBehavioral pattern; Cooperative translation; Engagement; Knowledge constructionNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTrace-otherTransitional.patternProcess.miningCollaboration2018Yang, Xianmin, Li, Jihong, Xing, Beibei
119Behavioral patterns of knowledge construction in online cooperative translation activitiesBehavioral pattern; Cooperative translation; Engagement; Knowledge constructionNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productEventTransitional.patternFrequent.sequence.miningCollaboration2018Yang, Xianmin, Li, Jihong, Xing, Beibei
119Behavioral patterns of knowledge construction in online cooperative translation activitiesBehavioral pattern; Cooperative translation; Engagement; Knowledge constructionNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productEventTransitional.patternProcess.miningCollaboration2018Yang, Xianmin, Li, Jihong, Xing, Beibei
119Behavioral patterns of knowledge construction in online cooperative translation activitiesBehavioral pattern; Cooperative translation; Engagement; Knowledge constructionNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTrace-forumTransitional.patternFrequent.sequence.miningCollaboration2018Yang, Xianmin, Li, Jihong, Xing, Beibei
119Behavioral patterns of knowledge construction in online cooperative translation activitiesBehavioral pattern; Cooperative translation; Engagement; Knowledge constructionNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTrace-forumTransitional.patternProcess.miningCollaboration2018Yang, Xianmin, Li, Jihong, Xing, Beibei
119Behavioral patterns of knowledge construction in online cooperative translation activitiesBehavioral pattern; Cooperative translation; Engagement; Knowledge constructionNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTrace-otherTransitional.patternFrequent.sequence.miningCollaboration2018Yang, Xianmin, Li, Jihong, Xing, Beibei
119Behavioral patterns of knowledge construction in online cooperative translation activitiesBehavioral pattern; Cooperative translation; Engagement; Knowledge constructionNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTrace-otherTransitional.patternProcess.miningCollaboration2018Yang, Xianmin, Li, Jihong, Xing, Beibei
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneNon-srl.indicators.identificationtime on taskLms.log.dataEventGroup.event.patternProcess.miningFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneNon-srl.indicators.identificationtime on taskLms.log.dataEventGroup.event.patternCluster.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneNon-srl.indicators.identificationtime on taskLms.log.dataEventGroup.event.patternVisualization.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneNon-srl.indicators.identificationtime on taskLms.log.dataEventTransitional.patternProcess.miningFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneNon-srl.indicators.identificationtime on taskLms.log.dataEventTransitional.patternCluster.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneNon-srl.indicators.identificationtime on taskLms.log.dataEventTransitional.patternVisualization.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneNon-srl.indicators.identificationtime on taskLms.log.dataTimeGroup.event.patternProcess.miningFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneNon-srl.indicators.identificationtime on taskLms.log.dataTimeGroup.event.patternCluster.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneNon-srl.indicators.identificationtime on taskLms.log.dataTimeGroup.event.patternVisualization.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneNon-srl.indicators.identificationtime on taskLms.log.dataTimeTransitional.patternProcess.miningFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneNon-srl.indicators.identificationtime on taskLms.log.dataTimeTransitional.patternCluster.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneNon-srl.indicators.identificationtime on taskLms.log.dataTimeTransitional.patternVisualization.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneNon-srl.indicators.identificationtime on taskLms.log.dataTrace-videoGroup.event.patternProcess.miningFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneNon-srl.indicators.identificationtime on taskLms.log.dataTrace-videoGroup.event.patternCluster.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneNon-srl.indicators.identificationtime on taskLms.log.dataTrace-videoGroup.event.patternVisualization.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneNon-srl.indicators.identificationtime on taskLms.log.dataTrace-videoTransitional.patternProcess.miningFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneNon-srl.indicators.identificationtime on taskLms.log.dataTrace-videoTransitional.patternCluster.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneNon-srl.indicators.identificationtime on taskLms.log.dataTrace-videoTransitional.patternVisualization.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneMethod.developmenttime on taskLms.log.dataEventGroup.event.patternProcess.miningFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneMethod.developmenttime on taskLms.log.dataEventGroup.event.patternCluster.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneMethod.developmenttime on taskLms.log.dataEventGroup.event.patternVisualization.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneMethod.developmenttime on taskLms.log.dataEventTransitional.patternProcess.miningFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneMethod.developmenttime on taskLms.log.dataEventTransitional.patternCluster.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneMethod.developmenttime on taskLms.log.dataEventTransitional.patternVisualization.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneMethod.developmenttime on taskLms.log.dataTimeGroup.event.patternProcess.miningFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneMethod.developmenttime on taskLms.log.dataTimeGroup.event.patternCluster.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneMethod.developmenttime on taskLms.log.dataTimeGroup.event.patternVisualization.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneMethod.developmenttime on taskLms.log.dataTimeTransitional.patternProcess.miningFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneMethod.developmenttime on taskLms.log.dataTimeTransitional.patternCluster.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneMethod.developmenttime on taskLms.log.dataTimeTransitional.patternVisualization.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneMethod.developmenttime on taskLms.log.dataTrace-videoGroup.event.patternProcess.miningFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneMethod.developmenttime on taskLms.log.dataTrace-videoGroup.event.patternCluster.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneMethod.developmenttime on taskLms.log.dataTrace-videoGroup.event.patternVisualization.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneMethod.developmenttime on taskLms.log.dataTrace-videoTransitional.patternProcess.miningFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneMethod.developmenttime on taskLms.log.dataTrace-videoTransitional.patternCluster.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
120Discovery and Temporal Analysis of Latent Study Patterns in MOOC Interaction SequencesEDM; LA; MOOCs; Cluster analysis ; learning analytics; markov model; sequence mining; study pattern; temporal analysisNoneMethod.developmenttime on taskLms.log.dataTrace-videoTransitional.patternVisualization.analysisFeedback2018Boroujeni, Mina Shirvani, Dillenbourg, Pierre
121Video-Based Question Generation for Mobile LearningMobile learning; SPARQL-based temporal query; Temporal-based Question; Video Annotation; Video FragmentNoneMethod.developmentNoneCustomized.log.dataEventOther.sequential.patternsBasic.statistical.analysisNo.learning.focus.outcome2018Nimkanjana, Klinsukon, Witosurapot, Suntorn
121Video-Based Question Generation for Mobile LearningMobile learning; SPARQL-based temporal query; Temporal-based Question; Video Annotation; Video FragmentNoneMethod.developmentNoneCustomized.log.dataTrace-videoOther.sequential.patternsBasic.statistical.analysisNo.learning.focus.outcome2018Nimkanjana, Klinsukon, Witosurapot, Suntorn
122Representing and Predicting Student Navigational Pathways in Online College Courseslong short-term memory; navigational prediction; online course; recurrent neural Network analysiss; representation learning; skip-gram; student modelingNoneMethod.developmentNoneLms.log.dataEventOther.sequential.patternsNeural.networkNo.learning.focus.outcome2018Yu, Renzhe, Jiang, Daokun, Warschauer, Mark
122Representing and Predicting Student Navigational Pathways in Online College Courseslong short-term memory; navigational prediction; online course; recurrent neural Network analysiss; representation learning; skip-gram; student modelingNoneMethod.developmentNoneLms.log.dataEventOther.sequential.patternsVisualization.analysisNo.learning.focus.outcome2018Yu, Renzhe, Jiang, Daokun, Warschauer, Mark
122Representing and Predicting Student Navigational Pathways in Online College Courseslong short-term memory; navigational prediction; online course; recurrent neural Network analysiss; representation learning; skip-gram; student modelingNoneAt-risk.student.identificationNoneLms.log.dataEventOther.sequential.patternsNeural.networkNo.learning.focus.outcome2018Yu, Renzhe, Jiang, Daokun, Warschauer, Mark
122Representing and Predicting Student Navigational Pathways in Online College Courseslong short-term memory; navigational prediction; online course; recurrent neural Network analysiss; representation learning; skip-gram; student modelingNoneAt-risk.student.identificationNoneLms.log.dataEventOther.sequential.patternsVisualization.analysisNo.learning.focus.outcome2018Yu, Renzhe, Jiang, Daokun, Warschauer, Mark
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataEventTransitional.patternProcess.miningCollaboration2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataEventSummativeProcess.miningLearning.indicators2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataEventSummativeProcess.miningCollaboration2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternProcess.miningLearning.indicators2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternProcess.miningCollaboration2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTrace-forumSummativeProcess.miningLearning.indicators2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTrace-forumSummativeProcess.miningCollaboration2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productEventTransitional.patternProcess.miningLearning.indicators2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productEventTransitional.patternProcess.miningCollaboration2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productEventSummativeProcess.miningLearning.indicators2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productEventSummativeProcess.miningCollaboration2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTrace-forumTransitional.patternProcess.miningLearning.indicators2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTrace-forumTransitional.patternProcess.miningCollaboration2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTrace-forumSummativeProcess.miningLearning.indicators2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTrace-forumSummativeProcess.miningCollaboration2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneGroup.comparisoncollaborative knowledge buildingLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneGroup.comparisoncollaborative knowledge buildingLms.log.dataEventTransitional.patternProcess.miningCollaboration2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneGroup.comparisoncollaborative knowledge buildingLms.log.dataEventSummativeProcess.miningLearning.indicators2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneGroup.comparisoncollaborative knowledge buildingLms.log.dataEventSummativeProcess.miningCollaboration2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneGroup.comparisoncollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternProcess.miningLearning.indicators2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneGroup.comparisoncollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternProcess.miningCollaboration2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneGroup.comparisoncollaborative knowledge buildingLms.log.dataTrace-forumSummativeProcess.miningLearning.indicators2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneGroup.comparisoncollaborative knowledge buildingLms.log.dataTrace-forumSummativeProcess.miningCollaboration2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneGroup.comparisoncollaborative knowledge buildingLearning.productEventTransitional.patternProcess.miningLearning.indicators2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneGroup.comparisoncollaborative knowledge buildingLearning.productEventTransitional.patternProcess.miningCollaboration2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneGroup.comparisoncollaborative knowledge buildingLearning.productEventSummativeProcess.miningLearning.indicators2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneGroup.comparisoncollaborative knowledge buildingLearning.productEventSummativeProcess.miningCollaboration2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneGroup.comparisoncollaborative knowledge buildingLearning.productTrace-forumTransitional.patternProcess.miningLearning.indicators2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneGroup.comparisoncollaborative knowledge buildingLearning.productTrace-forumTransitional.patternProcess.miningCollaboration2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneGroup.comparisoncollaborative knowledge buildingLearning.productTrace-forumSummativeProcess.miningLearning.indicators2018Bakla, Arif
123Learner-generated materials in a flipped pronunciation class: A sequential explanatory mixed-methods studyAuthoring tools; Flipped learning; Learner-generated content; Moodle; PronunciationNoneGroup.comparisoncollaborative knowledge buildingLearning.productTrace-forumSummativeProcess.miningCollaboration2018Bakla, Arif
124Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group SolutionsTime; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysisNoneGroup.comparisoncollaborative knowledge buildingLms.log.dataEventOther.sequential.patternsBasic.statistical.analysisLearning.indicators2018Chiu, MIng Ming
124Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group SolutionsTime; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysisNoneGroup.comparisoncollaborative knowledge buildingLms.log.dataEventSummativeBasic.statistical.analysisLearning.indicators2018Chiu, MIng Ming
124Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group SolutionsTime; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysisNoneGroup.comparisoncollaborative knowledge buildingLms.log.dataTrace-forumOther.sequential.patternsBasic.statistical.analysisLearning.indicators2018Chiu, MIng Ming
124Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group SolutionsTime; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysisNoneGroup.comparisoncollaborative knowledge buildingLms.log.dataTrace-forumSummativeBasic.statistical.analysisLearning.indicators2018Chiu, MIng Ming
124Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group SolutionsTime; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysisNoneGroup.comparisoncollaborative knowledge buildingLearning.productEventOther.sequential.patternsBasic.statistical.analysisLearning.indicators2018Chiu, MIng Ming
124Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group SolutionsTime; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysisNoneGroup.comparisoncollaborative knowledge buildingLearning.productEventSummativeBasic.statistical.analysisLearning.indicators2018Chiu, MIng Ming
124Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group SolutionsTime; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysisNoneGroup.comparisoncollaborative knowledge buildingLearning.productTrace-forumOther.sequential.patternsBasic.statistical.analysisLearning.indicators2018Chiu, MIng Ming
124Statistically Modelling Effects of Dynamic Processes on Outcomes: An Example of Discourse Sequences and Group SolutionsTime; hierarchicaly linear modelling; mathematical proof; multilevel modelling; sequential analysisNoneGroup.comparisoncollaborative knowledge buildingLearning.productTrace-forumSummativeBasic.statistical.analysisLearning.indicators2018Chiu, MIng Ming
125An empirical study of using sequential behavior pattern mining approach to predict learning stylesLearning styles; MBTI; Sequential behavior patterns; Sequential pattern miningNoneMethod.developmentNoneCustomized.log.dataEventEvent.sequenceOther.predictions.modelsNo.learning.focus.outcome2018Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125An empirical study of using sequential behavior pattern mining approach to predict learning stylesLearning styles; MBTI; Sequential behavior patterns; Sequential pattern miningNoneMethod.developmentNoneCustomized.log.dataEventOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2018Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125An empirical study of using sequential behavior pattern mining approach to predict learning stylesLearning styles; MBTI; Sequential behavior patterns; Sequential pattern miningNoneMethod.developmentNoneCustomized.log.dataTrace-quizEvent.sequenceOther.predictions.modelsNo.learning.focus.outcome2018Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125An empirical study of using sequential behavior pattern mining approach to predict learning stylesLearning styles; MBTI; Sequential behavior patterns; Sequential pattern miningNoneMethod.developmentNoneCustomized.log.dataTrace-quizOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2018Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125An empirical study of using sequential behavior pattern mining approach to predict learning stylesLearning styles; MBTI; Sequential behavior patterns; Sequential pattern miningNoneMethod.developmentNoneCustomized.log.dataTrace-readingEvent.sequenceOther.predictions.modelsNo.learning.focus.outcome2018Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125An empirical study of using sequential behavior pattern mining approach to predict learning stylesLearning styles; MBTI; Sequential behavior patterns; Sequential pattern miningNoneMethod.developmentNoneCustomized.log.dataTrace-readingOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2018Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125An empirical study of using sequential behavior pattern mining approach to predict learning stylesLearning styles; MBTI; Sequential behavior patterns; Sequential pattern miningNoneMethod.developmentNoneCustomized.log.dataTrace-exerciseEvent.sequenceOther.predictions.modelsNo.learning.focus.outcome2018Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125An empirical study of using sequential behavior pattern mining approach to predict learning stylesLearning styles; MBTI; Sequential behavior patterns; Sequential pattern miningNoneMethod.developmentNoneCustomized.log.dataTrace-exerciseOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2018Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125An empirical study of using sequential behavior pattern mining approach to predict learning stylesLearning styles; MBTI; Sequential behavior patterns; Sequential pattern miningNoneMethod.developmentNoneCustomized.log.dataTrace-forumEvent.sequenceOther.predictions.modelsNo.learning.focus.outcome2018Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125An empirical study of using sequential behavior pattern mining approach to predict learning stylesLearning styles; MBTI; Sequential behavior patterns; Sequential pattern miningNoneMethod.developmentNoneCustomized.log.dataTrace-forumOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2018Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125An empirical study of using sequential behavior pattern mining approach to predict learning stylesLearning styles; MBTI; Sequential behavior patterns; Sequential pattern miningNoneNon-srl.indicators.identificationNoneCustomized.log.dataEventEvent.sequenceOther.predictions.modelsNo.learning.focus.outcome2018Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125An empirical study of using sequential behavior pattern mining approach to predict learning stylesLearning styles; MBTI; Sequential behavior patterns; Sequential pattern miningNoneNon-srl.indicators.identificationNoneCustomized.log.dataEventOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2018Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125An empirical study of using sequential behavior pattern mining approach to predict learning stylesLearning styles; MBTI; Sequential behavior patterns; Sequential pattern miningNoneNon-srl.indicators.identificationNoneCustomized.log.dataTrace-quizEvent.sequenceOther.predictions.modelsNo.learning.focus.outcome2018Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125An empirical study of using sequential behavior pattern mining approach to predict learning stylesLearning styles; MBTI; Sequential behavior patterns; Sequential pattern miningNoneNon-srl.indicators.identificationNoneCustomized.log.dataTrace-quizOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2018Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125An empirical study of using sequential behavior pattern mining approach to predict learning stylesLearning styles; MBTI; Sequential behavior patterns; Sequential pattern miningNoneNon-srl.indicators.identificationNoneCustomized.log.dataTrace-readingEvent.sequenceOther.predictions.modelsNo.learning.focus.outcome2018Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125An empirical study of using sequential behavior pattern mining approach to predict learning stylesLearning styles; MBTI; Sequential behavior patterns; Sequential pattern miningNoneNon-srl.indicators.identificationNoneCustomized.log.dataTrace-readingOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2018Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125An empirical study of using sequential behavior pattern mining approach to predict learning stylesLearning styles; MBTI; Sequential behavior patterns; Sequential pattern miningNoneNon-srl.indicators.identificationNoneCustomized.log.dataTrace-exerciseEvent.sequenceOther.predictions.modelsNo.learning.focus.outcome2018Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125An empirical study of using sequential behavior pattern mining approach to predict learning stylesLearning styles; MBTI; Sequential behavior patterns; Sequential pattern miningNoneNon-srl.indicators.identificationNoneCustomized.log.dataTrace-exerciseOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2018Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125An empirical study of using sequential behavior pattern mining approach to predict learning stylesLearning styles; MBTI; Sequential behavior patterns; Sequential pattern miningNoneNon-srl.indicators.identificationNoneCustomized.log.dataTrace-forumEvent.sequenceOther.predictions.modelsNo.learning.focus.outcome2018Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
125An empirical study of using sequential behavior pattern mining approach to predict learning stylesLearning styles; MBTI; Sequential behavior patterns; Sequential pattern miningNoneNon-srl.indicators.identificationNoneCustomized.log.dataTrace-forumOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2018Fatahi, Somayeh, Shabanali-Fami, Faezeh, Moradi, Hadi
126Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analyticsEducational technology; curriculum analytics; early warning systems; survival analysis; undergraduate educationNoneAt-risk.student.identificationNoneLms.log.dataEventSummativeOther.predictions.modelsNo.learning.focus.outcome2018Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie
126Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analyticsEducational technology; curriculum analytics; early warning systems; survival analysis; undergraduate educationNoneAt-risk.student.identificationNoneLms.log.dataTimeSummativeOther.predictions.modelsNo.learning.focus.outcome2018Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie
126Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analyticsEducational technology; curriculum analytics; early warning systems; survival analysis; undergraduate educationNoneAt-risk.student.identificationNonePerformance.measuresEventSummativeOther.predictions.modelsNo.learning.focus.outcome2018Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie
126Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analyticsEducational technology; curriculum analytics; early warning systems; survival analysis; undergraduate educationNoneAt-risk.student.identificationNonePerformance.measuresTimeSummativeOther.predictions.modelsNo.learning.focus.outcome2018Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie
126Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analyticsEducational technology; curriculum analytics; early warning systems; survival analysis; undergraduate educationNoneAt-risk.student.identificationNoneLearner.characteristicsEventSummativeOther.predictions.modelsNo.learning.focus.outcome2018Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie
126Taken Together: Conceptualizing Students’ Concurrent Course Enrollment across the Post-Secondary Curriculum using temporal analyticsEducational technology; curriculum analytics; early warning systems; survival analysis; undergraduate educationNoneAt-risk.student.identificationNoneLearner.characteristicsTimeSummativeOther.predictions.modelsNo.learning.focus.outcome2018Brown, Michael, DeMonbrun, R. Matthew, Teasley, Stephanie
127Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Successhigher education; learning analytics; predictive modeling; self-regulated learning; student engagementNoneAt-risk.student.identificationsrlLms.log.dataEventSummativeBasic.statistical.analysisTime.on.learning2017Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Successhigher education; learning analytics; predictive modeling; self-regulated learning; student engagementNoneAt-risk.student.identificationsrlLms.log.dataTimeSummativeBasic.statistical.analysisTime.on.learning2017Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Successhigher education; learning analytics; predictive modeling; self-regulated learning; student engagementNoneAt-risk.student.identificationsrlLms.log.dataTrace-quizSummativeBasic.statistical.analysisTime.on.learning2017Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Successhigher education; learning analytics; predictive modeling; self-regulated learning; student engagementNoneAt-risk.student.identificationsrlLms.log.dataTrace-readingSummativeBasic.statistical.analysisTime.on.learning2017Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Successhigher education; learning analytics; predictive modeling; self-regulated learning; student engagementNoneAt-risk.student.identificationsrlPerformance.measuresEventSummativeBasic.statistical.analysisTime.on.learning2017Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Successhigher education; learning analytics; predictive modeling; self-regulated learning; student engagementNoneAt-risk.student.identificationsrlPerformance.measuresTimeSummativeBasic.statistical.analysisTime.on.learning2017Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Successhigher education; learning analytics; predictive modeling; self-regulated learning; student engagementNoneAt-risk.student.identificationsrlPerformance.measuresTrace-quizSummativeBasic.statistical.analysisTime.on.learning2017Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Successhigher education; learning analytics; predictive modeling; self-regulated learning; student engagementNoneAt-risk.student.identificationsrlPerformance.measuresTrace-readingSummativeBasic.statistical.analysisTime.on.learning2017Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Successhigher education; learning analytics; predictive modeling; self-regulated learning; student engagementNoneAt-risk.student.identificationsrlLearner.characteristicsEventSummativeBasic.statistical.analysisTime.on.learning2017Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Successhigher education; learning analytics; predictive modeling; self-regulated learning; student engagementNoneAt-risk.student.identificationsrlLearner.characteristicsTimeSummativeBasic.statistical.analysisTime.on.learning2017Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Successhigher education; learning analytics; predictive modeling; self-regulated learning; student engagementNoneAt-risk.student.identificationsrlLearner.characteristicsTrace-quizSummativeBasic.statistical.analysisTime.on.learning2017Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
127Using Predictive Analytics in a Self-Regulated Learning University Course to Promote Student Successhigher education; learning analytics; predictive modeling; self-regulated learning; student engagementNoneAt-risk.student.identificationsrlLearner.characteristicsTrace-readingSummativeBasic.statistical.analysisTime.on.learning2017Edwards, Rebecca L, Davis, Sarah K, Hadwin, Allyson F, Milford, Todd M
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneTime.to.interventionsrlCustomized.log.dataEventOther.sequential.patternsOther.predictions.modelsTime.on.learning2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneTime.to.interventionsrlCustomized.log.dataEventOther.sequential.patternsOther.predictions.modelsLearning.indicators2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneTime.to.interventionsrlCustomized.log.dataTimeOther.sequential.patternsOther.predictions.modelsTime.on.learning2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneTime.to.interventionsrlCustomized.log.dataTimeOther.sequential.patternsOther.predictions.modelsLearning.indicators2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneTime.to.interventionsrlPerformance.measuresEventOther.sequential.patternsOther.predictions.modelsTime.on.learning2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneTime.to.interventionsrlPerformance.measuresEventOther.sequential.patternsOther.predictions.modelsLearning.indicators2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneTime.to.interventionsrlPerformance.measuresTimeOther.sequential.patternsOther.predictions.modelsTime.on.learning2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneTime.to.interventionsrlPerformance.measuresTimeOther.sequential.patternsOther.predictions.modelsLearning.indicators2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneTime.to.interventionsrlSelf-reportedEventOther.sequential.patternsOther.predictions.modelsTime.on.learning2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneTime.to.interventionsrlSelf-reportedEventOther.sequential.patternsOther.predictions.modelsLearning.indicators2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneTime.to.interventionsrlSelf-reportedTimeOther.sequential.patternsOther.predictions.modelsTime.on.learning2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneTime.to.interventionsrlSelf-reportedTimeOther.sequential.patternsOther.predictions.modelsLearning.indicators2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneMethod.developmentsrlCustomized.log.dataEventOther.sequential.patternsOther.predictions.modelsTime.on.learning2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneMethod.developmentsrlCustomized.log.dataEventOther.sequential.patternsOther.predictions.modelsLearning.indicators2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneMethod.developmentsrlCustomized.log.dataTimeOther.sequential.patternsOther.predictions.modelsTime.on.learning2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneMethod.developmentsrlCustomized.log.dataTimeOther.sequential.patternsOther.predictions.modelsLearning.indicators2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneMethod.developmentsrlPerformance.measuresEventOther.sequential.patternsOther.predictions.modelsTime.on.learning2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneMethod.developmentsrlPerformance.measuresEventOther.sequential.patternsOther.predictions.modelsLearning.indicators2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneMethod.developmentsrlPerformance.measuresTimeOther.sequential.patternsOther.predictions.modelsTime.on.learning2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneMethod.developmentsrlPerformance.measuresTimeOther.sequential.patternsOther.predictions.modelsLearning.indicators2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneMethod.developmentsrlSelf-reportedEventOther.sequential.patternsOther.predictions.modelsTime.on.learning2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneMethod.developmentsrlSelf-reportedEventOther.sequential.patternsOther.predictions.modelsLearning.indicators2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneMethod.developmentsrlSelf-reportedTimeOther.sequential.patternsOther.predictions.modelsTime.on.learning2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
128Detecting Diligence with Online Behaviors on Intelligent Tutoring Systemsdiligence; intelligent tutoring systems; learning analytics; measurement; motivation; noncognitive factors; online behaviors; self-control; self-regulated learningNoneMethod.developmentsrlSelf-reportedTimeOther.sequential.patternsOther.predictions.modelsLearning.indicators2017Dang, Steven, Yudelson, Michael, Koedinger, Kenneth R
129Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods studyAdult learning; Distance education and telelearning; Lifelong learningNoneNon-srl.indicators.identificationotherLms.log.dataEventSummativeBasic.statistical.analysisLearning.indicators2017Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
129Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods studyAdult learning; Distance education and telelearning; Lifelong learningNoneNon-srl.indicators.identificationotherLms.log.dataTimeSummativeBasic.statistical.analysisLearning.indicators2017Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
129Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods studyAdult learning; Distance education and telelearning; Lifelong learningNoneNon-srl.indicators.identificationotherSelf-reportedEventSummativeBasic.statistical.analysisLearning.indicators2017Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
129Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods studyAdult learning; Distance education and telelearning; Lifelong learningNoneNon-srl.indicators.identificationotherSelf-reportedTimeSummativeBasic.statistical.analysisLearning.indicators2017Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
129Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods studyAdult learning; Distance education and telelearning; Lifelong learningNoneNon-srl.indicators.identificationotherLearner.characteristicsEventSummativeBasic.statistical.analysisLearning.indicators2017Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
129Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods studyAdult learning; Distance education and telelearning; Lifelong learningNoneNon-srl.indicators.identificationotherLearner.characteristicsTimeSummativeBasic.statistical.analysisLearning.indicators2017Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
129Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods studyAdult learning; Distance education and telelearning; Lifelong learningNoneNon-srl.indicators.identificationotherPerformance.measuresEventSummativeBasic.statistical.analysisLearning.indicators2017Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
129Learner profiles of attitudinal learning in a MOOC: An explanatory sequential mixed methods studyAdult learning; Distance education and telelearning; Lifelong learningNoneNon-srl.indicators.identificationotherPerformance.measuresTimeSummativeBasic.statistical.analysisLearning.indicators2017Watson, Sunnie Lee, Watson, William R, Yu, Ji Hyun, Alamri, Hamdan, Mueller, Chad
130Analyzing undergraduate students' performance using educational data miningClustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processesNoneMethod.developmentNonePerformance.measuresEventNoneOther.predictions.modelsNo.learning.focus.outcome2017Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130Analyzing undergraduate students' performance using educational data miningClustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processesNoneMethod.developmentNonePerformance.measuresEventNoneCluster.analysisNo.learning.focus.outcome2017Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130Analyzing undergraduate students' performance using educational data miningClustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processesNoneMethod.developmentNonePerformance.measuresEventNoneVisualization.analysisNo.learning.focus.outcome2017Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130Analyzing undergraduate students' performance using educational data miningClustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processesNoneAt-risk.student.identificationNonePerformance.measuresEventNoneOther.predictions.modelsNo.learning.focus.outcome2017Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130Analyzing undergraduate students' performance using educational data miningClustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processesNoneAt-risk.student.identificationNonePerformance.measuresEventNoneCluster.analysisNo.learning.focus.outcome2017Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130Analyzing undergraduate students' performance using educational data miningClustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processesNoneAt-risk.student.identificationNonePerformance.measuresEventNoneVisualization.analysisNo.learning.focus.outcome2017Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130Analyzing undergraduate students' performance using educational data miningClustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processesNoneGroup.comparisonNonePerformance.measuresEventNoneOther.predictions.modelsNo.learning.focus.outcome2017Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130Analyzing undergraduate students' performance using educational data miningClustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processesNoneGroup.comparisonNonePerformance.measuresEventNoneCluster.analysisNo.learning.focus.outcome2017Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
130Analyzing undergraduate students' performance using educational data miningClustering; Data mining; Decision trees; Performance prediction; Performance progression; Quality of educational processesNoneGroup.comparisonNonePerformance.measuresEventNoneVisualization.analysisNo.learning.focus.outcome2017Asif, Raheela, Merceron, Agathe, Ali, Syed Abbas, Haider, Najmi Ghani
131Gaining Insight by Transforming Between Temporal Representations of Human InteractionTemporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analysesNoneNon-srl.indicators.identificationaffective learning; collaborative knowledge buildingContextualEventOther.sequential.patternsNetwork.analysisLearning.indicators2017Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131Gaining Insight by Transforming Between Temporal Representations of Human InteractionTemporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analysesNoneNon-srl.indicators.identificationaffective learning; collaborative knowledge buildingContextualEventOther.sequential.patternsNetwork.analysisCollaboration2017Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131Gaining Insight by Transforming Between Temporal Representations of Human InteractionTemporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analysesNoneNon-srl.indicators.identificationaffective learning; collaborative knowledge buildingContextualEventOther.sequential.patternsVisualization.analysisLearning.indicators2017Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131Gaining Insight by Transforming Between Temporal Representations of Human InteractionTemporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analysesNoneNon-srl.indicators.identificationaffective learning; collaborative knowledge buildingContextualEventOther.sequential.patternsVisualization.analysisCollaboration2017Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131Gaining Insight by Transforming Between Temporal Representations of Human InteractionTemporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analysesNoneNon-srl.indicators.identificationaffective learning; collaborative knowledge buildingContextualTrace-otherOther.sequential.patternsNetwork.analysisLearning.indicators2017Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131Gaining Insight by Transforming Between Temporal Representations of Human InteractionTemporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analysesNoneNon-srl.indicators.identificationaffective learning; collaborative knowledge buildingContextualTrace-otherOther.sequential.patternsNetwork.analysisCollaboration2017Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131Gaining Insight by Transforming Between Temporal Representations of Human InteractionTemporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analysesNoneNon-srl.indicators.identificationaffective learning; collaborative knowledge buildingContextualTrace-otherOther.sequential.patternsVisualization.analysisLearning.indicators2017Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131Gaining Insight by Transforming Between Temporal Representations of Human InteractionTemporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analysesNoneNon-srl.indicators.identificationaffective learning; collaborative knowledge buildingContextualTrace-otherOther.sequential.patternsVisualization.analysisCollaboration2017Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131Gaining Insight by Transforming Between Temporal Representations of Human InteractionTemporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analysesNoneNon-srl.indicators.identificationaffective learning; collaborative knowledge buildingContextualTimeOther.sequential.patternsNetwork.analysisLearning.indicators2017Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131Gaining Insight by Transforming Between Temporal Representations of Human InteractionTemporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analysesNoneNon-srl.indicators.identificationaffective learning; collaborative knowledge buildingContextualTimeOther.sequential.patternsNetwork.analysisCollaboration2017Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131Gaining Insight by Transforming Between Temporal Representations of Human InteractionTemporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analysesNoneNon-srl.indicators.identificationaffective learning; collaborative knowledge buildingContextualTimeOther.sequential.patternsVisualization.analysisLearning.indicators2017Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
131Gaining Insight by Transforming Between Temporal Representations of Human InteractionTemporal representations; emotion; collaborative knowledge construction; connecting micro- and macro-analysesNoneNon-srl.indicators.identificationaffective learning; collaborative knowledge buildingContextualTimeOther.sequential.patternsVisualization.analysisCollaboration2017Lund, Kristine, Quignard, Mattieu, {Williamson Shaffer}, David
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataEventTransitional.patternProcess.miningCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataEventTransitional.patternBasic.statistical.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataEventTransitional.patternVisualization.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataEventSummativeProcess.miningCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataEventSummativeBasic.statistical.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataEventSummativeVisualization.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTrace-otherTransitional.patternProcess.miningCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTrace-otherTransitional.patternBasic.statistical.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTrace-otherTransitional.patternVisualization.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTrace-otherSummativeProcess.miningCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTrace-otherSummativeBasic.statistical.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTrace-otherSummativeVisualization.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTrace-videoTransitional.patternProcess.miningCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTrace-videoTransitional.patternBasic.statistical.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTrace-videoTransitional.patternVisualization.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTrace-videoSummativeProcess.miningCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTrace-videoSummativeBasic.statistical.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTrace-videoSummativeVisualization.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTrace-forumTransitional.patternProcess.miningCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTrace-forumTransitional.patternBasic.statistical.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTrace-forumTransitional.patternVisualization.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTrace-forumSummativeProcess.miningCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTrace-forumSummativeBasic.statistical.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTrace-forumSummativeVisualization.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTimeTransitional.patternProcess.miningCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTimeTransitional.patternBasic.statistical.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTimeTransitional.patternVisualization.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTimeSummativeProcess.miningCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTimeSummativeBasic.statistical.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningCustomized.log.dataTimeSummativeVisualization.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productEventTransitional.patternProcess.miningCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productEventTransitional.patternBasic.statistical.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productEventTransitional.patternVisualization.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productEventSummativeProcess.miningCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productEventSummativeBasic.statistical.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productEventSummativeVisualization.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTrace-otherTransitional.patternProcess.miningCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTrace-otherTransitional.patternBasic.statistical.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTrace-otherTransitional.patternVisualization.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTrace-otherSummativeProcess.miningCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTrace-otherSummativeBasic.statistical.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTrace-otherSummativeVisualization.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTrace-videoTransitional.patternProcess.miningCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTrace-videoTransitional.patternBasic.statistical.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTrace-videoTransitional.patternVisualization.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTrace-videoSummativeProcess.miningCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTrace-videoSummativeBasic.statistical.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTrace-videoSummativeVisualization.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTrace-forumTransitional.patternProcess.miningCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTrace-forumTransitional.patternBasic.statistical.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTrace-forumTransitional.patternVisualization.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTrace-forumSummativeProcess.miningCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTrace-forumSummativeBasic.statistical.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTrace-forumSummativeVisualization.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTimeTransitional.patternProcess.miningCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTimeTransitional.patternBasic.statistical.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTimeTransitional.patternVisualization.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTimeSummativeProcess.miningCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTimeSummativeBasic.statistical.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
132Exploring temporal sequences of regulatory phases and associated interactions in low- and high-challenge collaborative learning sessionsCollaborative learning; Interaction types; Process.mining; Self-regulated learning; Temporal patterns; Video dataNoneExploring.srl.processessrl; collaborative knowledge building; affective learningLearning.productTimeSummativeVisualization.analysisCollaboration2017Sobocinski, Marta, Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherCustomized.log.dataEventTransitional.patternProcess.miningLearning.indicators2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherCustomized.log.dataEventTransitional.patternProcess.miningCollaboration2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherCustomized.log.dataEventTransitional.patternVisualization.analysisLearning.indicators2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherCustomized.log.dataEventTransitional.patternVisualization.analysisCollaboration2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherCustomized.log.dataTrace-exerciseTransitional.patternProcess.miningLearning.indicators2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherCustomized.log.dataTrace-exerciseTransitional.patternProcess.miningCollaboration2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherCustomized.log.dataTrace-exerciseTransitional.patternVisualization.analysisLearning.indicators2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherCustomized.log.dataTrace-exerciseTransitional.patternVisualization.analysisCollaboration2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherCustomized.log.dataTrace-readingTransitional.patternProcess.miningLearning.indicators2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherCustomized.log.dataTrace-readingTransitional.patternProcess.miningCollaboration2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherCustomized.log.dataTrace-readingTransitional.patternVisualization.analysisLearning.indicators2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherCustomized.log.dataTrace-readingTransitional.patternVisualization.analysisCollaboration2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherCustomized.log.dataTrace-otherTransitional.patternProcess.miningLearning.indicators2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherCustomized.log.dataTrace-otherTransitional.patternProcess.miningCollaboration2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherCustomized.log.dataTrace-otherTransitional.patternVisualization.analysisLearning.indicators2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherCustomized.log.dataTrace-otherTransitional.patternVisualization.analysisCollaboration2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherPerformance.measuresEventTransitional.patternProcess.miningLearning.indicators2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherPerformance.measuresEventTransitional.patternProcess.miningCollaboration2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherPerformance.measuresEventTransitional.patternVisualization.analysisLearning.indicators2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherPerformance.measuresEventTransitional.patternVisualization.analysisCollaboration2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherPerformance.measuresTrace-exerciseTransitional.patternProcess.miningLearning.indicators2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherPerformance.measuresTrace-exerciseTransitional.patternProcess.miningCollaboration2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherPerformance.measuresTrace-exerciseTransitional.patternVisualization.analysisLearning.indicators2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherPerformance.measuresTrace-exerciseTransitional.patternVisualization.analysisCollaboration2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherPerformance.measuresTrace-readingTransitional.patternProcess.miningLearning.indicators2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherPerformance.measuresTrace-readingTransitional.patternProcess.miningCollaboration2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherPerformance.measuresTrace-readingTransitional.patternVisualization.analysisLearning.indicators2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherPerformance.measuresTrace-readingTransitional.patternVisualization.analysisCollaboration2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherPerformance.measuresTrace-otherTransitional.patternProcess.miningLearning.indicators2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherPerformance.measuresTrace-otherTransitional.patternProcess.miningCollaboration2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherPerformance.measuresTrace-otherTransitional.patternVisualization.analysisLearning.indicators2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
133Unfolding online learning behavioral patterns and their temporal changes of college students in SPOCsSPOC; Small private online courses; blended learning; online-learning behaviors; sequential analysisNoneGroup.comparisonotherPerformance.measuresTrace-otherTransitional.patternVisualization.analysisCollaboration2017Cheng, Hercy N.H., Liu, Zhi, Sun, Jianwen, Liu, Sanya, Yang, Zongkai
134Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourseFrequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analyticsNoneMethod.developmentcollaborative knowledge buildingLms.log.dataEventEvent.sequenceFrequent.sequence.miningTime.on.learning2017Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourseFrequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analyticsNoneMethod.developmentcollaborative knowledge buildingLms.log.dataEventEvent.sequenceProcess.miningTime.on.learning2017Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourseFrequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analyticsNoneMethod.developmentcollaborative knowledge buildingLms.log.dataEventTransitional.patternFrequent.sequence.miningTime.on.learning2017Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourseFrequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analyticsNoneMethod.developmentcollaborative knowledge buildingLms.log.dataEventTransitional.patternProcess.miningTime.on.learning2017Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourseFrequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analyticsNoneMethod.developmentcollaborative knowledge buildingLms.log.dataTrace-forumEvent.sequenceFrequent.sequence.miningTime.on.learning2017Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourseFrequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analyticsNoneMethod.developmentcollaborative knowledge buildingLms.log.dataTrace-forumEvent.sequenceProcess.miningTime.on.learning2017Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourseFrequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analyticsNoneMethod.developmentcollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternFrequent.sequence.miningTime.on.learning2017Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourseFrequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analyticsNoneMethod.developmentcollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternProcess.miningTime.on.learning2017Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourseFrequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analyticsNoneMethod.developmentcollaborative knowledge buildingLearning.productEventEvent.sequenceFrequent.sequence.miningTime.on.learning2017Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourseFrequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analyticsNoneMethod.developmentcollaborative knowledge buildingLearning.productEventEvent.sequenceProcess.miningTime.on.learning2017Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourseFrequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analyticsNoneMethod.developmentcollaborative knowledge buildingLearning.productEventTransitional.patternFrequent.sequence.miningTime.on.learning2017Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourseFrequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analyticsNoneMethod.developmentcollaborative knowledge buildingLearning.productEventTransitional.patternProcess.miningTime.on.learning2017Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourseFrequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analyticsNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-forumEvent.sequenceFrequent.sequence.miningTime.on.learning2017Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourseFrequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analyticsNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-forumEvent.sequenceProcess.miningTime.on.learning2017Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourseFrequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analyticsNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-forumTransitional.patternFrequent.sequence.miningTime.on.learning2017Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
134Two tales of time: uncovering the significance of sequential patterns among contribution types in knowledge-building discourseFrequent Sequence Mining; Lag-sequential Analysis; Temporality; knowledge building; learning analyticsNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-forumTransitional.patternProcess.miningTime.on.learning2017Chen, Bodong, Resendes, Monica, Chai, Ching Sing, Hong, Huang Yao
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherLms.log.dataEventSummativeVisualization.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherLms.log.dataEventSummativeVisualization.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherLms.log.dataEventSummativeNetwork.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherLms.log.dataEventSummativeNetwork.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherLms.log.dataEventSummativeOther.predictions.modelsTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherLms.log.dataEventSummativeOther.predictions.modelsCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherLms.log.dataTimeSummativeVisualization.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherLms.log.dataTimeSummativeVisualization.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherLms.log.dataTimeSummativeNetwork.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherLms.log.dataTimeSummativeNetwork.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherLms.log.dataTimeSummativeOther.predictions.modelsTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherLms.log.dataTimeSummativeOther.predictions.modelsCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherLms.log.dataTrace-otherSummativeVisualization.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherLms.log.dataTrace-otherSummativeVisualization.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherLms.log.dataTrace-otherSummativeNetwork.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherLms.log.dataTrace-otherSummativeNetwork.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherLms.log.dataTrace-otherSummativeOther.predictions.modelsTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherLms.log.dataTrace-otherSummativeOther.predictions.modelsCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherPerformance.measuresEventSummativeVisualization.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherPerformance.measuresEventSummativeVisualization.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherPerformance.measuresEventSummativeNetwork.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherPerformance.measuresEventSummativeNetwork.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherPerformance.measuresEventSummativeOther.predictions.modelsTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherPerformance.measuresEventSummativeOther.predictions.modelsCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherPerformance.measuresTimeSummativeVisualization.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherPerformance.measuresTimeSummativeVisualization.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherPerformance.measuresTimeSummativeNetwork.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherPerformance.measuresTimeSummativeNetwork.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherPerformance.measuresTimeSummativeOther.predictions.modelsTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherPerformance.measuresTimeSummativeOther.predictions.modelsCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherPerformance.measuresTrace-otherSummativeVisualization.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherPerformance.measuresTrace-otherSummativeVisualization.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherPerformance.measuresTrace-otherSummativeNetwork.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherPerformance.measuresTrace-otherSummativeNetwork.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherPerformance.measuresTrace-otherSummativeOther.predictions.modelsTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneMethod.developmentotherPerformance.measuresTrace-otherSummativeOther.predictions.modelsCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherLms.log.dataEventSummativeVisualization.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherLms.log.dataEventSummativeVisualization.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherLms.log.dataEventSummativeNetwork.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherLms.log.dataEventSummativeNetwork.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherLms.log.dataEventSummativeOther.predictions.modelsTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherLms.log.dataEventSummativeOther.predictions.modelsCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherLms.log.dataTimeSummativeVisualization.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherLms.log.dataTimeSummativeVisualization.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherLms.log.dataTimeSummativeNetwork.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherLms.log.dataTimeSummativeNetwork.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherLms.log.dataTimeSummativeOther.predictions.modelsTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherLms.log.dataTimeSummativeOther.predictions.modelsCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherLms.log.dataTrace-otherSummativeVisualization.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherLms.log.dataTrace-otherSummativeVisualization.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherLms.log.dataTrace-otherSummativeNetwork.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherLms.log.dataTrace-otherSummativeNetwork.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherLms.log.dataTrace-otherSummativeOther.predictions.modelsTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherLms.log.dataTrace-otherSummativeOther.predictions.modelsCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherPerformance.measuresEventSummativeVisualization.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherPerformance.measuresEventSummativeVisualization.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherPerformance.measuresEventSummativeNetwork.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherPerformance.measuresEventSummativeNetwork.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherPerformance.measuresEventSummativeOther.predictions.modelsTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherPerformance.measuresEventSummativeOther.predictions.modelsCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherPerformance.measuresTimeSummativeVisualization.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherPerformance.measuresTimeSummativeVisualization.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherPerformance.measuresTimeSummativeNetwork.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherPerformance.measuresTimeSummativeNetwork.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherPerformance.measuresTimeSummativeOther.predictions.modelsTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherPerformance.measuresTimeSummativeOther.predictions.modelsCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherPerformance.measuresTrace-otherSummativeVisualization.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherPerformance.measuresTrace-otherSummativeVisualization.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherPerformance.measuresTrace-otherSummativeNetwork.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherPerformance.measuresTrace-otherSummativeNetwork.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherPerformance.measuresTrace-otherSummativeOther.predictions.modelsTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneAt-risk.student.identificationotherPerformance.measuresTrace-otherSummativeOther.predictions.modelsCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherLms.log.dataEventSummativeVisualization.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherLms.log.dataEventSummativeVisualization.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherLms.log.dataEventSummativeNetwork.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherLms.log.dataEventSummativeNetwork.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherLms.log.dataEventSummativeOther.predictions.modelsTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherLms.log.dataEventSummativeOther.predictions.modelsCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherLms.log.dataTimeSummativeVisualization.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherLms.log.dataTimeSummativeVisualization.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherLms.log.dataTimeSummativeNetwork.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherLms.log.dataTimeSummativeNetwork.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherLms.log.dataTimeSummativeOther.predictions.modelsTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherLms.log.dataTimeSummativeOther.predictions.modelsCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherLms.log.dataTrace-otherSummativeVisualization.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherLms.log.dataTrace-otherSummativeVisualization.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherLms.log.dataTrace-otherSummativeNetwork.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherLms.log.dataTrace-otherSummativeNetwork.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherLms.log.dataTrace-otherSummativeOther.predictions.modelsTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherLms.log.dataTrace-otherSummativeOther.predictions.modelsCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherPerformance.measuresEventSummativeVisualization.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherPerformance.measuresEventSummativeVisualization.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherPerformance.measuresEventSummativeNetwork.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherPerformance.measuresEventSummativeNetwork.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherPerformance.measuresEventSummativeOther.predictions.modelsTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherPerformance.measuresEventSummativeOther.predictions.modelsCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherPerformance.measuresTimeSummativeVisualization.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherPerformance.measuresTimeSummativeVisualization.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherPerformance.measuresTimeSummativeNetwork.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherPerformance.measuresTimeSummativeNetwork.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherPerformance.measuresTimeSummativeOther.predictions.modelsTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherPerformance.measuresTimeSummativeOther.predictions.modelsCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherPerformance.measuresTrace-otherSummativeVisualization.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherPerformance.measuresTrace-otherSummativeVisualization.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherPerformance.measuresTrace-otherSummativeNetwork.analysisTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherPerformance.measuresTrace-otherSummativeNetwork.analysisCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherPerformance.measuresTrace-otherSummativeOther.predictions.modelsTime.on.learning2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
135An Instructor Dashboard for Real-Time Analytics in Interactive Programming Assignmentsdashboards; introductory programming; learning analytics; machine learning; peer tutorsNoneTime.to.interventionotherPerformance.measuresTrace-otherSummativeOther.predictions.modelsCollaboration2017Diana, Nicholas, Eagle, Michael, Stamper, John, Grover, Shuchi, Bienkowski, Marie, Basu, Satabdi
136Using Programming Process Data to Detect Differences in Students' Patterns of Programmingeducational data mining; learning analytics; predictive measures; programming Basic statistical analysise modelNoneMethod.developmentNoneLms.log.dataEventEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2017Carter, Adam Scott, Hundhausen, Christopher David
136Using Programming Process Data to Detect Differences in Students' Patterns of Programmingeducational data mining; learning analytics; predictive measures; programming Basic statistical analysise modelNoneMethod.developmentNoneLms.log.dataEventSummativeFrequent.sequence.miningNo.learning.focus.outcome2017Carter, Adam Scott, Hundhausen, Christopher David
136Using Programming Process Data to Detect Differences in Students' Patterns of Programmingeducational data mining; learning analytics; predictive measures; programming Basic statistical analysise modelNoneMethod.developmentNonePerformance.measuresEventEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2017Carter, Adam Scott, Hundhausen, Christopher David
136Using Programming Process Data to Detect Differences in Students' Patterns of Programmingeducational data mining; learning analytics; predictive measures; programming Basic statistical analysise modelNoneMethod.developmentNonePerformance.measuresEventSummativeFrequent.sequence.miningNo.learning.focus.outcome2017Carter, Adam Scott, Hundhausen, Christopher David
137A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal DependenciesEmbodied cognition; embodied learning; hidden Markov models; optimal matching; temporal analyticsNoneMethod.developmentotherMultimodalEventTransitional.patternProcess.miningTime.on.learning2017Andrade, Alejandro, Danish, Joshua A., Maltese, Adam V.
137A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal DependenciesEmbodied cognition; embodied learning; hidden Markov models; optimal matching; temporal analyticsNoneMethod.developmentotherMultimodalEventTransitional.patternVisualization.analysisTime.on.learning2017Andrade, Alejandro, Danish, Joshua A., Maltese, Adam V.
137A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal DependenciesEmbodied cognition; embodied learning; hidden Markov models; optimal matching; temporal analyticsNoneMethod.developmentotherMultimodalTrace-otherTransitional.patternProcess.miningTime.on.learning2017Andrade, Alejandro, Danish, Joshua A., Maltese, Adam V.
137A Measurement Model of Gestures in an Embodied Learning Environment: Accounting for Temporal DependenciesEmbodied cognition; embodied learning; hidden Markov models; optimal matching; temporal analyticsNoneMethod.developmentotherMultimodalTrace-otherTransitional.patternVisualization.analysisTime.on.learning2017Andrade, Alejandro, Danish, Joshua A., Maltese, Adam V.
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataEventEvent.sequenceCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataEventEvent.sequenceVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataEventGroup.event.patternFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataEventGroup.event.patternCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataEventGroup.event.patternVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-readingEvent.sequenceCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-readingEvent.sequenceVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-readingGroup.event.patternCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-readingGroup.event.patternVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-quizEvent.sequenceCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-quizEvent.sequenceVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-quizGroup.event.patternFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-quizGroup.event.patternCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-quizGroup.event.patternVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-videoEvent.sequenceFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-videoEvent.sequenceCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-videoEvent.sequenceVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-videoGroup.event.patternFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-videoGroup.event.patternCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-videoGroup.event.patternVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-otherEvent.sequenceFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-otherEvent.sequenceCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-otherEvent.sequenceVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-otherGroup.event.patternFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-otherGroup.event.patternCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlLms.log.dataTrace-otherGroup.event.patternVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresEventEvent.sequenceFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresEventEvent.sequenceCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresEventEvent.sequenceVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresEventGroup.event.patternFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresEventGroup.event.patternCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresEventGroup.event.patternVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-readingEvent.sequenceCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-readingEvent.sequenceVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-readingGroup.event.patternCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-readingGroup.event.patternVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-quizEvent.sequenceCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-quizEvent.sequenceVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-quizGroup.event.patternFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-quizGroup.event.patternCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-quizGroup.event.patternVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-videoEvent.sequenceFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-videoEvent.sequenceCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-videoEvent.sequenceVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-videoGroup.event.patternFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-videoGroup.event.patternCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-videoGroup.event.patternVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-otherEvent.sequenceFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-otherEvent.sequenceCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-otherEvent.sequenceVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-otherGroup.event.patternFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-otherGroup.event.patternCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneMethod.developmentsrlPerformance.measuresTrace-otherGroup.event.patternVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataEventEvent.sequenceCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataEventEvent.sequenceVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataEventGroup.event.patternFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataEventGroup.event.patternCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataEventGroup.event.patternVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-readingEvent.sequenceCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-readingEvent.sequenceVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-readingGroup.event.patternCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-readingGroup.event.patternVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-quizEvent.sequenceCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-quizEvent.sequenceVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-quizGroup.event.patternFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-quizGroup.event.patternCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-quizGroup.event.patternVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-videoEvent.sequenceFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-videoEvent.sequenceCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-videoEvent.sequenceVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-videoGroup.event.patternFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-videoGroup.event.patternCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-videoGroup.event.patternVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-otherEvent.sequenceFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-otherEvent.sequenceCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-otherEvent.sequenceVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-otherGroup.event.patternFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-otherGroup.event.patternCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlLms.log.dataTrace-otherGroup.event.patternVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresEventEvent.sequenceFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresEventEvent.sequenceCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresEventEvent.sequenceVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresEventGroup.event.patternFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresEventGroup.event.patternCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresEventGroup.event.patternVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-readingEvent.sequenceCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-readingEvent.sequenceVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-readingGroup.event.patternCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-readingGroup.event.patternVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-quizEvent.sequenceCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-quizEvent.sequenceVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-quizGroup.event.patternFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-quizGroup.event.patternCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-quizGroup.event.patternVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-videoEvent.sequenceFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-videoEvent.sequenceCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-videoEvent.sequenceVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-videoGroup.event.patternFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-videoGroup.event.patternCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-videoGroup.event.patternVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-otherEvent.sequenceFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-otherEvent.sequenceCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-otherEvent.sequenceVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-otherGroup.event.patternFrequent.sequence.miningLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-otherGroup.event.patternCluster.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
138Learning analytics to unveil learning strategies in a flipped classroomlearning strategies; sequence analysis; self-regulated learning; learning analyticsNoneExploring.srl.processessrlPerformance.measuresTrace-otherGroup.event.patternVisualization.analysisLearning.indicators2017Jovanovic, Jelena, Gavsevic, Dragan, Dawson, Shane, Pardo, Abelardo, Mirriahi, Negin
139Shapes of Educational Data in an Online Calculus CourseMarkov chain; clickstream; sequence analysisNoneMethod.developmentNoneLms.log.dataEventEvent.sequenceOther.predictions.modelsLearning.indicators2017Caprotti, Olga
139Shapes of Educational Data in an Online Calculus CourseMarkov chain; clickstream; sequence analysisNoneMethod.developmentNoneLms.log.dataEventEvent.sequenceVisualization.analysisLearning.indicators2017Caprotti, Olga
139Shapes of Educational Data in an Online Calculus CourseMarkov chain; clickstream; sequence analysisNoneMethod.developmentNoneLms.log.dataEventOther.sequential.patternsOther.predictions.modelsLearning.indicators2017Caprotti, Olga
139Shapes of Educational Data in an Online Calculus CourseMarkov chain; clickstream; sequence analysisNoneMethod.developmentNoneLms.log.dataEventOther.sequential.patternsVisualization.analysisLearning.indicators2017Caprotti, Olga
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventSummativeContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventSummativeContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventSummativeCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventSummativeCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventSummativeVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventSummativeVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventGroup.event.patternContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventGroup.event.patternContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventGroup.event.patternCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventGroup.event.patternCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventGroup.event.patternVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventGroup.event.patternVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTimeSummativeContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTimeSummativeContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTimeSummativeCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTimeSummativeCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTimeSummativeVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTimeSummativeVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTimeGroup.event.patternContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTimeGroup.event.patternContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTimeGroup.event.patternCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTimeGroup.event.patternCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTimeGroup.event.patternVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTimeGroup.event.patternVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumSummativeContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumSummativeContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumSummativeCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumSummativeCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumSummativeVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumSummativeVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumGroup.event.patternContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumGroup.event.patternContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumGroup.event.patternCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumGroup.event.patternCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumGroup.event.patternVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumGroup.event.patternVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventSummativeContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventSummativeContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventSummativeCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventSummativeCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventSummativeVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventSummativeVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventGroup.event.patternContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventGroup.event.patternContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventGroup.event.patternCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventGroup.event.patternCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventGroup.event.patternVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventGroup.event.patternVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTimeSummativeContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTimeSummativeContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTimeSummativeCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTimeSummativeCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTimeSummativeVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTimeSummativeVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTimeGroup.event.patternContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTimeGroup.event.patternContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTimeGroup.event.patternCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTimeGroup.event.patternCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTimeGroup.event.patternVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTimeGroup.event.patternVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumSummativeContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumSummativeContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumSummativeCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumSummativeCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumSummativeVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumSummativeVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumGroup.event.patternContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumGroup.event.patternContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumGroup.event.patternCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumGroup.event.patternCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumGroup.event.patternVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumGroup.event.patternVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataEventSummativeContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataEventSummativeContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataEventSummativeCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataEventSummativeCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataEventSummativeVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataEventSummativeVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataEventGroup.event.patternContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataEventGroup.event.patternContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataEventGroup.event.patternCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataEventGroup.event.patternCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataEventGroup.event.patternVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataEventGroup.event.patternVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTimeSummativeContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTimeSummativeContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTimeSummativeCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTimeSummativeCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTimeSummativeVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTimeSummativeVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTimeGroup.event.patternContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTimeGroup.event.patternContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTimeGroup.event.patternCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTimeGroup.event.patternCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTimeGroup.event.patternVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTimeGroup.event.patternVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTrace-forumSummativeContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTrace-forumSummativeContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTrace-forumSummativeCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTrace-forumSummativeCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTrace-forumSummativeVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTrace-forumSummativeVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTrace-forumGroup.event.patternContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTrace-forumGroup.event.patternContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTrace-forumGroup.event.patternCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTrace-forumGroup.event.patternCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTrace-forumGroup.event.patternVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTrace-forumGroup.event.patternVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productEventSummativeContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productEventSummativeContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productEventSummativeCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productEventSummativeCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productEventSummativeVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productEventSummativeVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productEventGroup.event.patternContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productEventGroup.event.patternContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productEventGroup.event.patternCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productEventGroup.event.patternCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productEventGroup.event.patternVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productEventGroup.event.patternVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTimeSummativeContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTimeSummativeContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTimeSummativeCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTimeSummativeCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTimeSummativeVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTimeSummativeVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTimeGroup.event.patternContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTimeGroup.event.patternContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTimeGroup.event.patternCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTimeGroup.event.patternCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTimeGroup.event.patternVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTimeGroup.event.patternVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTrace-forumSummativeContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTrace-forumSummativeContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTrace-forumSummativeCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTrace-forumSummativeCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTrace-forumSummativeVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTrace-forumSummativeVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTrace-forumGroup.event.patternContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTrace-forumGroup.event.patternContent.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTrace-forumGroup.event.patternCluster.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTrace-forumGroup.event.patternCluster.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTrace-forumGroup.event.patternVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
140Promising Ideas for Collective Advancement of Communal Knowledge Using Temporal Analytics and Cluster AnalysisTemporal analytics; cluster analysis; idea analysis; knowledge building discourse; machine learning; promising ideasNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTrace-forumGroup.event.patternVisualization.analysisLearning.indicators2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataEventEvent.sequenceCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataEventGroup.event.patternFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataEventGroup.event.patternCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataEventSummativeFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataEventSummativeCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-exerciseEvent.sequenceFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-exerciseEvent.sequenceCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-exerciseGroup.event.patternFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-exerciseGroup.event.patternCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-exerciseSummativeFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-exerciseSummativeCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-readingEvent.sequenceCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-readingGroup.event.patternCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-readingSummativeFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-readingSummativeCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-videoEvent.sequenceFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-videoEvent.sequenceCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-videoGroup.event.patternFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-videoGroup.event.patternCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-videoSummativeFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-videoSummativeCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-quizEvent.sequenceCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-quizGroup.event.patternFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-quizGroup.event.patternCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-quizSummativeFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherLms.log.dataTrace-quizSummativeCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedEventEvent.sequenceFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedEventEvent.sequenceCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedEventGroup.event.patternFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedEventGroup.event.patternCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedEventSummativeFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedEventSummativeCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-exerciseEvent.sequenceFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-exerciseEvent.sequenceCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-exerciseGroup.event.patternFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-exerciseGroup.event.patternCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-exerciseSummativeFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-exerciseSummativeCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-readingEvent.sequenceCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-readingGroup.event.patternCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-readingSummativeFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-readingSummativeCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-videoEvent.sequenceFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-videoEvent.sequenceCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-videoGroup.event.patternFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-videoGroup.event.patternCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-videoSummativeFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-videoSummativeCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-quizEvent.sequenceCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-quizGroup.event.patternFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-quizGroup.event.patternCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-quizSummativeFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneNon-srl.indicators.identificationotherSelf-reportedTrace-quizSummativeCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataEventEvent.sequenceCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataEventGroup.event.patternFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataEventGroup.event.patternCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataEventSummativeFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataEventSummativeCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-exerciseEvent.sequenceFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-exerciseEvent.sequenceCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-exerciseGroup.event.patternFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-exerciseGroup.event.patternCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-exerciseSummativeFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-exerciseSummativeCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-readingEvent.sequenceCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-readingGroup.event.patternCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-readingSummativeFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-readingSummativeCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-videoEvent.sequenceFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-videoEvent.sequenceCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-videoGroup.event.patternFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-videoGroup.event.patternCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-videoSummativeFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-videoSummativeCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-quizEvent.sequenceCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-quizGroup.event.patternFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-quizGroup.event.patternCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-quizSummativeFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherLms.log.dataTrace-quizSummativeCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedEventEvent.sequenceFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedEventEvent.sequenceCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedEventGroup.event.patternFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedEventGroup.event.patternCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedEventSummativeFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedEventSummativeCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-exerciseEvent.sequenceFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-exerciseEvent.sequenceCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-exerciseGroup.event.patternFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-exerciseGroup.event.patternCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-exerciseSummativeFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-exerciseSummativeCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-readingEvent.sequenceFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-readingEvent.sequenceCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-readingGroup.event.patternFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-readingGroup.event.patternCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-readingSummativeFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-readingSummativeCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-videoEvent.sequenceFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-videoEvent.sequenceCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-videoGroup.event.patternFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-videoGroup.event.patternCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-videoSummativeFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-videoSummativeCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-quizEvent.sequenceCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-quizGroup.event.patternFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-quizGroup.event.patternCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-quizSummativeFrequent.sequence.miningLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
141Detecting Learning Strategies with Analytics: Links with Self-reported Measures and Academic PerformanceLearning analytics; approaches to learning; learning strategy; self-reported measuresNoneGroup.comparisonotherSelf-reportedTrace-quizSummativeCluster.analysisLearning.indicators2017Gasevic, Dragan, Jovanovic, Jelena, Pardo, Abelardo, Dawson, Shane
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.socio-dynamicssrl; collaborative knowledge buildingContextualEventTransitional.patternQualitative.analysisCollaboration2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.socio-dynamicssrl; collaborative knowledge buildingContextualEventTransitional.patternQualitative.analysisLearning.indicators2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.socio-dynamicssrl; collaborative knowledge buildingContextualEventTransitional.patternBasic.statistical.analysisCollaboration2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.socio-dynamicssrl; collaborative knowledge buildingContextualEventTransitional.patternBasic.statistical.analysisLearning.indicators2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.socio-dynamicssrl; collaborative knowledge buildingContextualEventSummativeQualitative.analysisCollaboration2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.socio-dynamicssrl; collaborative knowledge buildingContextualEventSummativeQualitative.analysisLearning.indicators2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.socio-dynamicssrl; collaborative knowledge buildingContextualEventSummativeBasic.statistical.analysisCollaboration2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.socio-dynamicssrl; collaborative knowledge buildingContextualEventSummativeBasic.statistical.analysisLearning.indicators2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.socio-dynamicssrl; collaborative knowledge buildingContextualTrace-otherTransitional.patternQualitative.analysisCollaboration2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.socio-dynamicssrl; collaborative knowledge buildingContextualTrace-otherTransitional.patternQualitative.analysisLearning.indicators2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.socio-dynamicssrl; collaborative knowledge buildingContextualTrace-otherTransitional.patternBasic.statistical.analysisCollaboration2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.socio-dynamicssrl; collaborative knowledge buildingContextualTrace-otherTransitional.patternBasic.statistical.analysisLearning.indicators2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.socio-dynamicssrl; collaborative knowledge buildingContextualTrace-otherSummativeQualitative.analysisCollaboration2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.socio-dynamicssrl; collaborative knowledge buildingContextualTrace-otherSummativeQualitative.analysisLearning.indicators2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.socio-dynamicssrl; collaborative knowledge buildingContextualTrace-otherSummativeBasic.statistical.analysisCollaboration2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.socio-dynamicssrl; collaborative knowledge buildingContextualTrace-otherSummativeBasic.statistical.analysisLearning.indicators2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.srl.processessrl; collaborative knowledge buildingContextualEventTransitional.patternQualitative.analysisCollaboration2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.srl.processessrl; collaborative knowledge buildingContextualEventTransitional.patternQualitative.analysisLearning.indicators2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.srl.processessrl; collaborative knowledge buildingContextualEventTransitional.patternBasic.statistical.analysisCollaboration2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.srl.processessrl; collaborative knowledge buildingContextualEventTransitional.patternBasic.statistical.analysisLearning.indicators2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.srl.processessrl; collaborative knowledge buildingContextualEventSummativeQualitative.analysisCollaboration2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.srl.processessrl; collaborative knowledge buildingContextualEventSummativeQualitative.analysisLearning.indicators2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.srl.processessrl; collaborative knowledge buildingContextualEventSummativeBasic.statistical.analysisCollaboration2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.srl.processessrl; collaborative knowledge buildingContextualEventSummativeBasic.statistical.analysisLearning.indicators2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.srl.processessrl; collaborative knowledge buildingContextualTrace-otherTransitional.patternQualitative.analysisCollaboration2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.srl.processessrl; collaborative knowledge buildingContextualTrace-otherTransitional.patternQualitative.analysisLearning.indicators2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.srl.processessrl; collaborative knowledge buildingContextualTrace-otherTransitional.patternBasic.statistical.analysisCollaboration2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.srl.processessrl; collaborative knowledge buildingContextualTrace-otherTransitional.patternBasic.statistical.analysisLearning.indicators2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.srl.processessrl; collaborative knowledge buildingContextualTrace-otherSummativeQualitative.analysisCollaboration2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.srl.processessrl; collaborative knowledge buildingContextualTrace-otherSummativeQualitative.analysisLearning.indicators2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.srl.processessrl; collaborative knowledge buildingContextualTrace-otherSummativeBasic.statistical.analysisCollaboration2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
142Co-regulation and knowledge construction in an online synchronous problem based learning settingCo-regulation; Collaborative knowledge building; Problem-based learning; Self-regulated learningNoneExploring.srl.processessrl; collaborative knowledge buildingContextualTrace-otherSummativeBasic.statistical.analysisLearning.indicators2017Lee, Lila, Lajoie, Susanne P., Poitras, Eric G., Nkangu, Miriam, Doleck, Tenzin
143Role Modelling in MOOC Discussion ForumsDiscussion Forums; Social Network Analysis; Temporal dataNoneExploring.socio-dynamicsotherLearning.productEventSummativeFrequent.sequence.miningCourse.design2017Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143Role Modelling in MOOC Discussion ForumsDiscussion Forums; Social Network Analysis; Temporal dataNoneExploring.socio-dynamicsotherLearning.productEventSummativeCluster.analysisCourse.design2017Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143Role Modelling in MOOC Discussion ForumsDiscussion Forums; Social Network Analysis; Temporal dataNoneExploring.socio-dynamicsotherLearning.productEventGroup.event.patternFrequent.sequence.miningCourse.design2017Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143Role Modelling in MOOC Discussion ForumsDiscussion Forums; Social Network Analysis; Temporal dataNoneExploring.socio-dynamicsotherLearning.productEventGroup.event.patternCluster.analysisCourse.design2017Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143Role Modelling in MOOC Discussion ForumsDiscussion Forums; Social Network Analysis; Temporal dataNoneExploring.socio-dynamicsotherLearning.productTrace-forumSummativeFrequent.sequence.miningCourse.design2017Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143Role Modelling in MOOC Discussion ForumsDiscussion Forums; Social Network Analysis; Temporal dataNoneExploring.socio-dynamicsotherLearning.productTrace-forumSummativeCluster.analysisCourse.design2017Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143Role Modelling in MOOC Discussion ForumsDiscussion Forums; Social Network Analysis; Temporal dataNoneExploring.socio-dynamicsotherLearning.productTrace-forumGroup.event.patternFrequent.sequence.miningCourse.design2017Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143Role Modelling in MOOC Discussion ForumsDiscussion Forums; Social Network Analysis; Temporal dataNoneExploring.socio-dynamicsotherLearning.productTrace-forumGroup.event.patternCluster.analysisCourse.design2017Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143Role Modelling in MOOC Discussion ForumsDiscussion Forums; Social Network Analysis; Temporal dataNoneNon-srl.indicators.identificationotherLearning.productEventSummativeFrequent.sequence.miningCourse.design2017Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143Role Modelling in MOOC Discussion ForumsDiscussion Forums; Social Network Analysis; Temporal dataNoneNon-srl.indicators.identificationotherLearning.productEventSummativeCluster.analysisCourse.design2017Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143Role Modelling in MOOC Discussion ForumsDiscussion Forums; Social Network Analysis; Temporal dataNoneNon-srl.indicators.identificationotherLearning.productEventGroup.event.patternFrequent.sequence.miningCourse.design2017Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143Role Modelling in MOOC Discussion ForumsDiscussion Forums; Social Network Analysis; Temporal dataNoneNon-srl.indicators.identificationotherLearning.productEventGroup.event.patternCluster.analysisCourse.design2017Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143Role Modelling in MOOC Discussion ForumsDiscussion Forums; Social Network Analysis; Temporal dataNoneNon-srl.indicators.identificationotherLearning.productTrace-forumSummativeFrequent.sequence.miningCourse.design2017Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143Role Modelling in MOOC Discussion ForumsDiscussion Forums; Social Network Analysis; Temporal dataNoneNon-srl.indicators.identificationotherLearning.productTrace-forumSummativeCluster.analysisCourse.design2017Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143Role Modelling in MOOC Discussion ForumsDiscussion Forums; Social Network Analysis; Temporal dataNoneNon-srl.indicators.identificationotherLearning.productTrace-forumGroup.event.patternFrequent.sequence.miningCourse.design2017Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
143Role Modelling in MOOC Discussion ForumsDiscussion Forums; Social Network Analysis; Temporal dataNoneNon-srl.indicators.identificationotherLearning.productTrace-forumGroup.event.patternCluster.analysisCourse.design2017Hecking, Tobias, Chounta, Irene Angelica, Hoppe, H. Ulrich
144Mining frequent learning pathways from a large educational datasetGraph mining; Learning pathways; Process.mining; Sequence miningNoneMethod.developmentNoneLms.log.dataEventGroup.event.patternCluster.analysisNo.learning.focus.outcome2017Patel, Nirmal, Sellman, Collin, Lomas, Derek
144Mining frequent learning pathways from a large educational datasetGraph mining; Learning pathways; Process.mining; Sequence miningNoneMethod.developmentNoneLms.log.dataEventGroup.event.patternProcess.miningNo.learning.focus.outcome2017Patel, Nirmal, Sellman, Collin, Lomas, Derek
144Mining frequent learning pathways from a large educational datasetGraph mining; Learning pathways; Process.mining; Sequence miningNoneMethod.developmentNoneLms.log.dataEventGroup.event.patternVisualization.analysisNo.learning.focus.outcome2017Patel, Nirmal, Sellman, Collin, Lomas, Derek
144Mining frequent learning pathways from a large educational datasetGraph mining; Learning pathways; Process.mining; Sequence miningNoneMethod.developmentNoneLms.log.dataEventOther.sequential.patternsCluster.analysisNo.learning.focus.outcome2017Patel, Nirmal, Sellman, Collin, Lomas, Derek
144Mining frequent learning pathways from a large educational datasetGraph mining; Learning pathways; Process.mining; Sequence miningNoneMethod.developmentNoneLms.log.dataEventOther.sequential.patternsProcess.miningNo.learning.focus.outcome2017Patel, Nirmal, Sellman, Collin, Lomas, Derek
144Mining frequent learning pathways from a large educational datasetGraph mining; Learning pathways; Process.mining; Sequence miningNoneMethod.developmentNoneLms.log.dataEventOther.sequential.patternsVisualization.analysisNo.learning.focus.outcome2017Patel, Nirmal, Sellman, Collin, Lomas, Derek
145Sequence modelling for analysing student interaction with educational systemsClustering; Markov chains; Sequence modellingNoneMethod.developmentNoneLms.log.dataEventOther.sequential.patternsCluster.analysisNo.learning.focus.outcome2017Hansen, Christian, Hansen, Casper, Hjuler, Niklas, Alstrup, Stephen, Lioma, Christina
145Sequence modelling for analysing student interaction with educational systemsClustering; Markov chains; Sequence modellingNoneMethod.developmentNoneLms.log.dataEventOther.sequential.patternsProcess.miningNo.learning.focus.outcome2017Hansen, Christian, Hansen, Casper, Hjuler, Niklas, Alstrup, Stephen, Lioma, Christina
145Sequence modelling for analysing student interaction with educational systemsClustering; Markov chains; Sequence modellingNoneMethod.developmentNoneLms.log.dataEventOther.sequential.patternsVisualization.analysisNo.learning.focus.outcome2017Hansen, Christian, Hansen, Casper, Hjuler, Niklas, Alstrup, Stephen, Lioma, Christina
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventSummativeCluster.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventSummativeCluster.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventSummativeVisualization.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventSummativeVisualization.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventGroup.event.patternCluster.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventGroup.event.patternCluster.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventGroup.event.patternVisualization.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventGroup.event.patternVisualization.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTimeSummativeCluster.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTimeSummativeCluster.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTimeSummativeVisualization.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTimeSummativeVisualization.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTimeGroup.event.patternCluster.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTimeGroup.event.patternCluster.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTimeGroup.event.patternVisualization.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTimeGroup.event.patternVisualization.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-videoSummativeCluster.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-videoSummativeCluster.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-videoSummativeVisualization.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-videoSummativeVisualization.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-videoGroup.event.patternCluster.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-videoGroup.event.patternCluster.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-videoGroup.event.patternVisualization.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-videoGroup.event.patternVisualization.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventSummativeCluster.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventSummativeCluster.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventSummativeVisualization.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventSummativeVisualization.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventGroup.event.patternCluster.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventGroup.event.patternCluster.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventGroup.event.patternVisualization.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventGroup.event.patternVisualization.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTimeSummativeCluster.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTimeSummativeCluster.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTimeSummativeVisualization.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTimeSummativeVisualization.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTimeGroup.event.patternCluster.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTimeGroup.event.patternCluster.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTimeGroup.event.patternVisualization.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTimeGroup.event.patternVisualization.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-videoSummativeCluster.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-videoSummativeCluster.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-videoSummativeVisualization.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-videoSummativeVisualization.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-videoGroup.event.patternCluster.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-videoGroup.event.patternCluster.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-videoGroup.event.patternVisualization.analysisTime.on.learning2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
146Dynamics of MOOC Discussion ForumsMOOCs; content analysis; discussion forum; massive open online courses; social Network analysis; temporal analysisNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-videoGroup.event.patternVisualization.analysisCollaboration2017Boroujeni, Mina Shirvani, Hecking, Tobias, Hoppe, H Ulrich, Dillenbourg, Pierre
147What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutorcorpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessmentNoneMethod.developmentotherCustomized.log.dataEventNoneVisualization.analysisTime.on.learning2017Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutorcorpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessmentNoneMethod.developmentotherCustomized.log.dataEventNoneVisualization.analysisLearning.indicators2017Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutorcorpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessmentNoneMethod.developmentotherCustomized.log.dataEventNoneBasic.statistical.analysisTime.on.learning2017Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutorcorpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessmentNoneMethod.developmentotherCustomized.log.dataEventNoneBasic.statistical.analysisLearning.indicators2017Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutorcorpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessmentNoneMethod.developmentotherCustomized.log.dataTrace-readingNoneVisualization.analysisTime.on.learning2017Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutorcorpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessmentNoneMethod.developmentotherCustomized.log.dataTrace-readingNoneVisualization.analysisLearning.indicators2017Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutorcorpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessmentNoneMethod.developmentotherCustomized.log.dataTrace-readingNoneBasic.statistical.analysisTime.on.learning2017Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutorcorpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessmentNoneMethod.developmentotherCustomized.log.dataTrace-readingNoneBasic.statistical.analysisLearning.indicators2017Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutorcorpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessmentNoneMethod.developmentotherCustomized.log.dataTrace-quizNoneVisualization.analysisTime.on.learning2017Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutorcorpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessmentNoneMethod.developmentotherCustomized.log.dataTrace-quizNoneVisualization.analysisLearning.indicators2017Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutorcorpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessmentNoneMethod.developmentotherCustomized.log.dataTrace-quizNoneBasic.statistical.analysisTime.on.learning2017Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutorcorpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessmentNoneMethod.developmentotherCustomized.log.dataTrace-quizNoneBasic.statistical.analysisLearning.indicators2017Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutorcorpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessmentNoneMethod.developmentotherCustomized.log.dataTimeNoneVisualization.analysisTime.on.learning2017Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutorcorpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessmentNoneMethod.developmentotherCustomized.log.dataTimeNoneVisualization.analysisLearning.indicators2017Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutorcorpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessmentNoneMethod.developmentotherCustomized.log.dataTimeNoneBasic.statistical.analysisTime.on.learning2017Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
147What'd You Say Again? Recurrence Quantification Analysis as a Method for Analyzing the Dynamics of Discourse in a Reading Strategy Tutorcorpus linguistics; dynamics; intelligent tutoring systems; natural language processing; reading; stealth assessmentNoneMethod.developmentotherCustomized.log.dataTimeNoneBasic.statistical.analysisLearning.indicators2017Allen, Laura K, Perret, Cecile, Likens, Aaron, McNamara, Danielle S
148Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Textsacademic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analyticsNoneMethod.developmentotherLms.log.dataEventEvent.sequenceContent.analysisNo.learning.focus.outcome2017Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Textsacademic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analyticsNoneMethod.developmentotherLms.log.dataEventEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2017Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Textsacademic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analyticsNoneMethod.developmentotherLms.log.dataTrace-exerciseEvent.sequenceContent.analysisNo.learning.focus.outcome2017Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Textsacademic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analyticsNoneMethod.developmentotherLms.log.dataTrace-exerciseEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2017Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Textsacademic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analyticsNoneMethod.developmentotherLms.log.dataTrace-readingEvent.sequenceContent.analysisNo.learning.focus.outcome2017Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Textsacademic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analyticsNoneMethod.developmentotherLms.log.dataTrace-readingEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2017Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Textsacademic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analyticsNoneMethod.developmentotherLms.log.dataTrace-otherEvent.sequenceContent.analysisNo.learning.focus.outcome2017Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Textsacademic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analyticsNoneMethod.developmentotherLms.log.dataTrace-otherEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2017Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Textsacademic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analyticsNoneMethod.developmentotherLearning.productEventEvent.sequenceContent.analysisNo.learning.focus.outcome2017Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Textsacademic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analyticsNoneMethod.developmentotherLearning.productEventEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2017Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Textsacademic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analyticsNoneMethod.developmentotherLearning.productTrace-exerciseEvent.sequenceContent.analysisNo.learning.focus.outcome2017Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Textsacademic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analyticsNoneMethod.developmentotherLearning.productTrace-exerciseEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2017Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Textsacademic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analyticsNoneMethod.developmentotherLearning.productTrace-readingEvent.sequenceContent.analysisNo.learning.focus.outcome2017Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Textsacademic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analyticsNoneMethod.developmentotherLearning.productTrace-readingEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2017Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Textsacademic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analyticsNoneMethod.developmentotherLearning.productTrace-otherEvent.sequenceContent.analysisNo.learning.focus.outcome2017Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
148Towards Mining Sequences and Dispersion of Rhetorical Moves in Student Written Textsacademic writing; learning analytics; process mining; rhetorical moves; sequence mining; temporal analysis; text mining; writing analyticsNoneMethod.developmentotherLearning.productTrace-otherEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2017Knight, Simon, Martinez-Maldonado, Roberto, Gibson, Andrew, {Buckingham Shum}, Simon
149Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activitiesComputer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategiesNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2017Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activitiesComputer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategiesNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventTransitional.patternProcess.miningCollaboration2017Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activitiesComputer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategiesNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventTransitional.patternVisualization.analysisLearning.indicators2017Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activitiesComputer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategiesNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventTransitional.patternVisualization.analysisCollaboration2017Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activitiesComputer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategiesNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternProcess.miningLearning.indicators2017Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activitiesComputer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategiesNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternProcess.miningCollaboration2017Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activitiesComputer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategiesNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternVisualization.analysisLearning.indicators2017Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activitiesComputer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategiesNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternVisualization.analysisCollaboration2017Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activitiesComputer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategiesNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventTransitional.patternProcess.miningLearning.indicators2017Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activitiesComputer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategiesNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventTransitional.patternProcess.miningCollaboration2017Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activitiesComputer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategiesNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventTransitional.patternVisualization.analysisLearning.indicators2017Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activitiesComputer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategiesNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventTransitional.patternVisualization.analysisCollaboration2017Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activitiesComputer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategiesNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumTransitional.patternProcess.miningLearning.indicators2017Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activitiesComputer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategiesNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumTransitional.patternProcess.miningCollaboration2017Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activitiesComputer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategiesNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumTransitional.patternVisualization.analysisLearning.indicators2017Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
149Interactive Network analysiss and social knowledge construction behavioral patterns in primary school teachers' online collaborative learning activitiesComputer-mediated communication; Cooperative/collaborative learning; Learning communities; Teaching/learning strategiesNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumTransitional.patternVisualization.analysisCollaboration2017Zhang, Si, Liu, Qingtang, Chen, Wenli, Wang, Qiyun, Huang, Zhifang
150A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errorsComputer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussionsNoneNon-srl.indicators.identificationotherLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2017Jeong, Allan, Li, Haiying, Pan, Andy Jiaren
150A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errorsComputer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussionsNoneNon-srl.indicators.identificationotherLms.log.dataTimeTransitional.patternProcess.miningLearning.indicators2017Jeong, Allan, Li, Haiying, Pan, Andy Jiaren
150A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errorsComputer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussionsNoneNon-srl.indicators.identificationotherLms.log.dataTrace-forumTransitional.patternProcess.miningLearning.indicators2017Jeong, Allan, Li, Haiying, Pan, Andy Jiaren
150A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errorsComputer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussionsNoneNon-srl.indicators.identificationotherLearning.productEventTransitional.patternProcess.miningLearning.indicators2017Jeong, Allan, Li, Haiying, Pan, Andy Jiaren
150A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errorsComputer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussionsNoneNon-srl.indicators.identificationotherLearning.productTimeTransitional.patternProcess.miningLearning.indicators2017Jeong, Allan, Li, Haiying, Pan, Andy Jiaren
150A sequential analysis of responses in online debates to postings of students exhibiting high versus low grammar and spelling errorsComputer-supported ;collaborative argumentation ; Discourse analysis ; Critical thinking ; Online discussionsNoneNon-srl.indicators.identificationotherLearning.productTrace-forumTransitional.patternProcess.miningLearning.indicators2017Jeong, Allan, Li, Haiying, Pan, Andy Jiaren
151Students' Careers Analysis: A Process Mining Approacheducational mining; process mining; students' career analysisNoneMethod.developmentNonePerformance.measuresEventSummativeProcess.miningNo.learning.focus.outcome2017Cameranesi, Marco, Diamantini, Claudia, Genga, Laura, Potena, Domenico
151Students' Careers Analysis: A Process Mining Approacheducational mining; process mining; students' career analysisNoneMethod.developmentNonePerformance.measuresEventSummativeVisualization.analysisNo.learning.focus.outcome2017Cameranesi, Marco, Diamantini, Claudia, Genga, Laura, Potena, Domenico
151Students' Careers Analysis: A Process Mining Approacheducational mining; process mining; students' career analysisNoneMethod.developmentNonePerformance.measuresTimeSummativeProcess.miningNo.learning.focus.outcome2017Cameranesi, Marco, Diamantini, Claudia, Genga, Laura, Potena, Domenico
151Students' Careers Analysis: A Process Mining Approacheducational mining; process mining; students' career analysisNoneMethod.developmentNonePerformance.measuresTimeSummativeVisualization.analysisNo.learning.focus.outcome2017Cameranesi, Marco, Diamantini, Claudia, Genga, Laura, Potena, Domenico
152Understanding Student Interactions in Capstone Courses to Improve Learning Experiencescapstone; cloud-based mobile system; computing majors; data science; education; empirical software engineering; process mining; project-based learningNoneMethod.developmentNoneCustomized.log.dataEventTransitional.patternProcess.miningCourse.design2017Neyem, Andres, Diaz-Mosquera, Juan, Munoz-Gama, Jorge, Navon, Jaime
152Understanding Student Interactions in Capstone Courses to Improve Learning Experiencescapstone; cloud-based mobile system; computing majors; data science; education; empirical software engineering; process mining; project-based learningNoneMethod.developmentNoneCustomized.log.dataEventTransitional.patternVisualization.analysisCourse.design2017Neyem, Andres, Diaz-Mosquera, Juan, Munoz-Gama, Jorge, Navon, Jaime
152Understanding Student Interactions in Capstone Courses to Improve Learning Experiencescapstone; cloud-based mobile system; computing majors; data science; education; empirical software engineering; process mining; project-based learningNoneMethod.developmentNoneLearner.characteristicsEventTransitional.patternProcess.miningCourse.design2017Neyem, Andres, Diaz-Mosquera, Juan, Munoz-Gama, Jorge, Navon, Jaime
152Understanding Student Interactions in Capstone Courses to Improve Learning Experiencescapstone; cloud-based mobile system; computing majors; data science; education; empirical software engineering; process mining; project-based learningNoneMethod.developmentNoneLearner.characteristicsEventTransitional.patternVisualization.analysisCourse.design2017Neyem, Andres, Diaz-Mosquera, Juan, Munoz-Gama, Jorge, Navon, Jaime
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventOther.sequential.patternsContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventOther.sequential.patternsNetwork.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventOther.sequential.patternsVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventSummativeContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventSummativeNetwork.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataEventSummativeVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumOther.sequential.patternsContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumOther.sequential.patternsNetwork.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumOther.sequential.patternsVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumSummativeContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumSummativeNetwork.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-forumSummativeVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-otherOther.sequential.patternsContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-otherOther.sequential.patternsNetwork.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-otherOther.sequential.patternsVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-otherSummativeContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-otherSummativeNetwork.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLms.log.dataTrace-otherSummativeVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventOther.sequential.patternsContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventOther.sequential.patternsNetwork.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventOther.sequential.patternsVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventSummativeContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventSummativeNetwork.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventSummativeVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumOther.sequential.patternsContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumOther.sequential.patternsNetwork.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumOther.sequential.patternsVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumSummativeContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumSummativeNetwork.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumSummativeVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-otherOther.sequential.patternsContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-otherOther.sequential.patternsNetwork.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-otherOther.sequential.patternsVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-otherSummativeContent.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-otherSummativeNetwork.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
153Temporal Analytics with Discourse Analysis: Tracing Ideas and Impact on Communal Discoursediscourse analysis; idea measurement; learning analytics; social Network analysis analysis; temporalityNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-otherSummativeVisualization.analysisCollaboration2017Lee, Alwyn Vwen Yen, Tan, Seng Chee
154Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary studyBlog; Collaborative learning; Behavioral pattern; Instructional strategyNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataEventTransitional.patternProcess.miningCourse.design2017Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi
154Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary studyBlog; Collaborative learning; Behavioral pattern; Instructional strategyNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTimeTransitional.patternProcess.miningCourse.design2017Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi
154Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary studyBlog; Collaborative learning; Behavioral pattern; Instructional strategyNoneNon-srl.indicators.identificationcollaborative knowledge buildingLms.log.dataTrace-forumTransitional.patternProcess.miningCourse.design2017Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi
154Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary studyBlog; Collaborative learning; Behavioral pattern; Instructional strategyNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productEventTransitional.patternProcess.miningCourse.design2017Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi
154Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary studyBlog; Collaborative learning; Behavioral pattern; Instructional strategyNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTimeTransitional.patternProcess.miningCourse.design2017Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi
154Analyzing the knowledge construction and cognitive patterns of blog-based instructional activities using four frequent interactive strategies (problem solving, peer assessment, role playing and peer tutoring): a preliminary studyBlog; Collaborative learning; Behavioral pattern; Instructional strategyNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTrace-forumTransitional.patternProcess.miningCourse.design2017Wang, Shu-Ming, Hou, Huei-Tse, Wu, Sheng-Yi
155Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patternsApplications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategiesNoneNon-srl.indicators.identificationgame-based learningCustomized.log.dataEventTransitional.patternProcess.miningLearning.indicators2017Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
155Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patternsApplications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategiesNoneNon-srl.indicators.identificationgame-based learningCustomized.log.dataEventTransitional.patternVisualization.analysisLearning.indicators2017Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
155Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patternsApplications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategiesNoneNon-srl.indicators.identificationgame-based learningCustomized.log.dataTrace-otherTransitional.patternProcess.miningLearning.indicators2017Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
155Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patternsApplications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategiesNoneNon-srl.indicators.identificationgame-based learningCustomized.log.dataTrace-otherTransitional.patternVisualization.analysisLearning.indicators2017Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
155Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patternsApplications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategiesNoneNon-srl.indicators.identificationgame-based learningPerformance.measuresEventTransitional.patternProcess.miningLearning.indicators2017Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
155Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patternsApplications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategiesNoneNon-srl.indicators.identificationgame-based learningPerformance.measuresEventTransitional.patternVisualization.analysisLearning.indicators2017Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
155Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patternsApplications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategiesNoneNon-srl.indicators.identificationgame-based learningPerformance.measuresTrace-otherTransitional.patternProcess.miningLearning.indicators2017Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
155Interaction of problem-based gaming and learning anxiety in language students' English listening performance and progressive behavioral patternsApplications in subject areas; Elementary education; Interactive learning environments; Teaching/learning strategiesNoneNon-srl.indicators.identificationgame-based learningPerformance.measuresTrace-otherTransitional.patternVisualization.analysisLearning.indicators2017Hwang, Gwo-Jen, Hsu, Ting-Chia, Lai, Chiu-Lin, Hsueh, Ching-Jung
156Influences of an inquiry-based ubiquitous gaming design on students’ learning achievements, motivation, behavioral patterns, and tendency towards critical thinking and problem solvingNoneNoneNon-srl.indicators.identificationgame-based learningCustomized.log.dataEventTransitional.patternProcess.miningLearning.indicators2017Hwang, Gwo-Jen, Chen, Chih-Hung
156Influences of an inquiry-based ubiquitous gaming design on students’ learning achievements, motivation, behavioral patterns, and tendency towards critical thinking and problem solvingNoneNoneNon-srl.indicators.identificationgame-based learningCustomized.log.dataTrace-otherTransitional.patternProcess.miningLearning.indicators2017Hwang, Gwo-Jen, Chen, Chih-Hung
156Influences of an inquiry-based ubiquitous gaming design on students’ learning achievements, motivation, behavioral patterns, and tendency towards critical thinking and problem solvingNoneNoneNon-srl.indicators.identificationgame-based learningPerformance.measuresEventTransitional.patternProcess.miningLearning.indicators2017Hwang, Gwo-Jen, Chen, Chih-Hung
156Influences of an inquiry-based ubiquitous gaming design on students’ learning achievements, motivation, behavioral patterns, and tendency towards critical thinking and problem solvingNoneNoneNon-srl.indicators.identificationgame-based learningPerformance.measuresTrace-otherTransitional.patternProcess.miningLearning.indicators2017Hwang, Gwo-Jen, Chen, Chih-Hung
157Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learningCo-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysisNoneExploring.srl.processesSRLContextualEventTransitional.patternProcess.miningLearning.indicators2017Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna
157Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learningCo-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysisNoneExploring.srl.processesSRLContextualEventTransitional.patternProcess.miningCollaboration2017Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna
157Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learningCo-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysisNoneExploring.srl.processesSRLContextualTrace-forumTransitional.patternProcess.miningLearning.indicators2017Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna
157Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learningCo-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysisNoneExploring.srl.processesSRLContextualTrace-forumTransitional.patternProcess.miningCollaboration2017Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna
157Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learningCo-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysisNoneExploring.srl.processesSRLContextualTimeTransitional.patternProcess.miningLearning.indicators2017Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna
157Capturing temporal and sequential patterns of self-, co-, and socially shared regulation in the context of collaborative learningCo-regulation; Collaborative learning; Metacognition; Self-regulated learning; Socially shared regulation; Temporal and sequential analysisNoneExploring.srl.processesSRLContextualTimeTransitional.patternProcess.miningCollaboration2017Malmberg, Jonna, J{\"a}rvel{\"a}, Sanna, J{\"a}rvenoja, Hanna
158An analysis of student collaborative problem solving activities mediated by collaborative simulationsCollaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulationNoneExploring.socio-dynamicscollaborative knowledge buildingCustomized.log.dataEventTransitional.patternProcess.miningCollaboration2017Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli
158An analysis of student collaborative problem solving activities mediated by collaborative simulationsCollaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulationNoneExploring.socio-dynamicscollaborative knowledge buildingCustomized.log.dataTrace-forumTransitional.patternProcess.miningCollaboration2017Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli
158An analysis of student collaborative problem solving activities mediated by collaborative simulationsCollaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulationNoneExploring.socio-dynamicscollaborative knowledge buildingCustomized.log.dataTrace-exerciseTransitional.patternProcess.miningCollaboration2017Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli
158An analysis of student collaborative problem solving activities mediated by collaborative simulationsCollaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulationNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productEventTransitional.patternProcess.miningCollaboration2017Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli
158An analysis of student collaborative problem solving activities mediated by collaborative simulationsCollaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulationNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-forumTransitional.patternProcess.miningCollaboration2017Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli
158An analysis of student collaborative problem solving activities mediated by collaborative simulationsCollaboration pattern; Collaborative problem solving; Discourse analysis; Lag sequential analysis; Science simulationNoneExploring.socio-dynamicscollaborative knowledge buildingLearning.productTrace-exerciseTransitional.patternProcess.miningCollaboration2017Chang, Chia-Jung, Chang, Ming-Hua, Chiu, Bing-Cheng, Liu, Chen-Chung, {Fan Chiang}, Shih-Hsun, Wen, Cai-Ting, Hwang, Fu-Kwun, Wu, Ying-Tien, Chao, Po-Yao, Lai, Chia-Hsi, Wu, Su-Wen, Chang, Chih-Kang, Chen, Wenli
159The Changing Patterns of MOOC Discoursediscourse complexity; discussion forums; learning at scale; moocs; on-topic discussionNoneExploring.socio-dynamicsNoneLms.log.dataEventSummativeBasic.statistical.analysisNo.learning.focus.outcome2017Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan
159The Changing Patterns of MOOC Discoursediscourse complexity; discussion forums; learning at scale; moocs; on-topic discussionNoneExploring.socio-dynamicsNoneLms.log.dataTrace-forumSummativeBasic.statistical.analysisNo.learning.focus.outcome2017Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan
159The Changing Patterns of MOOC Discoursediscourse complexity; discussion forums; learning at scale; moocs; on-topic discussionNoneExploring.socio-dynamicsNoneLms.log.dataTimeSummativeBasic.statistical.analysisNo.learning.focus.outcome2017Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan
159The Changing Patterns of MOOC Discoursediscourse complexity; discussion forums; learning at scale; moocs; on-topic discussionNoneExploring.socio-dynamicsNoneLearning.productEventSummativeBasic.statistical.analysisNo.learning.focus.outcome2017Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan
159The Changing Patterns of MOOC Discoursediscourse complexity; discussion forums; learning at scale; moocs; on-topic discussionNoneExploring.socio-dynamicsNoneLearning.productTrace-forumSummativeBasic.statistical.analysisNo.learning.focus.outcome2017Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan
159The Changing Patterns of MOOC Discoursediscourse complexity; discussion forums; learning at scale; moocs; on-topic discussionNoneExploring.socio-dynamicsNoneLearning.productTimeSummativeBasic.statistical.analysisNo.learning.focus.outcome2017Dowell, Nia M M, Brooks, Christopher, Kovanovic, Vitomir, Joksimovic, Srecko, Gavsevic, Dragan
160Modeling Student Learning Styles in MOOCsbehavior modeling; moocs; probabilistic modeling; sequential data miningNoneMethod.developmentNoneLms.log.dataEventOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2017Shi, Yuling, Peng, Zhiyong, Wang, Hongning
160Modeling Student Learning Styles in MOOCsbehavior modeling; moocs; probabilistic modeling; sequential data miningNoneMethod.developmentNoneLms.log.dataEventOther.sequential.patternsVisualization.analysisNo.learning.focus.outcome2017Shi, Yuling, Peng, Zhiyong, Wang, Hongning
160Modeling Student Learning Styles in MOOCsbehavior modeling; moocs; probabilistic modeling; sequential data miningNoneMethod.developmentNoneLearning.productEventOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2017Shi, Yuling, Peng, Zhiyong, Wang, Hongning
160Modeling Student Learning Styles in MOOCsbehavior modeling; moocs; probabilistic modeling; sequential data miningNoneMethod.developmentNoneLearning.productEventOther.sequential.patternsVisualization.analysisNo.learning.focus.outcome2017Shi, Yuling, Peng, Zhiyong, Wang, Hongning
161Data-driven modeling of learners' individual differences for predicting engagement and success in online learningIndividual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern miningNoneMethod.developmentNoneLms.log.dataEventEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2021Akhuseyinoglu, Kamil, Brusilovsky, Peter
161Data-driven modeling of learners' individual differences for predicting engagement and success in online learningIndividual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern miningNoneMethod.developmentNoneLms.log.dataEventEvent.sequenceCluster.analysisNo.learning.focus.outcome2021Akhuseyinoglu, Kamil, Brusilovsky, Peter
161Data-driven modeling of learners' individual differences for predicting engagement and success in online learningIndividual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern miningNoneMethod.developmentNoneLms.log.dataEventEvent.sequenceOther.predictions.modelsNo.learning.focus.outcome2021Akhuseyinoglu, Kamil, Brusilovsky, Peter
161Data-driven modeling of learners' individual differences for predicting engagement and success in online learningIndividual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern miningNoneMethod.developmentNonePerformance.measuresEventEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2021Akhuseyinoglu, Kamil, Brusilovsky, Peter
161Data-driven modeling of learners' individual differences for predicting engagement and success in online learningIndividual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern miningNoneMethod.developmentNonePerformance.measuresEventEvent.sequenceCluster.analysisNo.learning.focus.outcome2021Akhuseyinoglu, Kamil, Brusilovsky, Peter
161Data-driven modeling of learners' individual differences for predicting engagement and success in online learningIndividual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern miningNoneMethod.developmentNonePerformance.measuresEventEvent.sequenceOther.predictions.modelsNo.learning.focus.outcome2021Akhuseyinoglu, Kamil, Brusilovsky, Peter
161Data-driven modeling of learners' individual differences for predicting engagement and success in online learningIndividual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern miningNoneGroup.comparisonNoneLms.log.dataEventEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2021Akhuseyinoglu, Kamil, Brusilovsky, Peter
161Data-driven modeling of learners' individual differences for predicting engagement and success in online learningIndividual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern miningNoneGroup.comparisonNoneLms.log.dataEventEvent.sequenceCluster.analysisNo.learning.focus.outcome2021Akhuseyinoglu, Kamil, Brusilovsky, Peter
161Data-driven modeling of learners' individual differences for predicting engagement and success in online learningIndividual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern miningNoneGroup.comparisonNoneLms.log.dataEventEvent.sequenceOther.predictions.modelsNo.learning.focus.outcome2021Akhuseyinoglu, Kamil, Brusilovsky, Peter
161Data-driven modeling of learners' individual differences for predicting engagement and success in online learningIndividual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern miningNoneGroup.comparisonNonePerformance.measuresEventEvent.sequenceFrequent.sequence.miningNo.learning.focus.outcome2021Akhuseyinoglu, Kamil, Brusilovsky, Peter
161Data-driven modeling of learners' individual differences for predicting engagement and success in online learningIndividual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern miningNoneGroup.comparisonNonePerformance.measuresEventEvent.sequenceCluster.analysisNo.learning.focus.outcome2021Akhuseyinoglu, Kamil, Brusilovsky, Peter
161Data-driven modeling of learners' individual differences for predicting engagement and success in online learningIndividual differences; Learner modeling; Learning technology; Online practice; SQL; Sequential pattern miningNoneGroup.comparisonNonePerformance.measuresEventEvent.sequenceOther.predictions.modelsNo.learning.focus.outcome2021Akhuseyinoglu, Kamil, Brusilovsky, Peter
162Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning EnvironmentsDashboard; EVE; LMS; learning; personalized; teachingNoneMethod.developmentNoneCustomized.log.dataEventSummativeBasic.statistical.analysisTime.on.learning2021Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
162Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning EnvironmentsDashboard; EVE; LMS; learning; personalized; teachingNoneMethod.developmentNoneCustomized.log.dataEventSummativeBasic.statistical.analysisCourse.design2021Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
162Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning EnvironmentsDashboard; EVE; LMS; learning; personalized; teachingNoneMethod.developmentNoneCustomized.log.dataTimeSummativeBasic.statistical.analysisTime.on.learning2021Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
162Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning EnvironmentsDashboard; EVE; LMS; learning; personalized; teachingNoneMethod.developmentNoneCustomized.log.dataTimeSummativeBasic.statistical.analysisCourse.design2021Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
162Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning EnvironmentsDashboard; EVE; LMS; learning; personalized; teachingNoneMethod.developmentNonePerformance.measuresEventSummativeBasic.statistical.analysisTime.on.learning2021Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
162Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning EnvironmentsDashboard; EVE; LMS; learning; personalized; teachingNoneMethod.developmentNonePerformance.measuresEventSummativeBasic.statistical.analysisCourse.design2021Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
162Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning EnvironmentsDashboard; EVE; LMS; learning; personalized; teachingNoneMethod.developmentNonePerformance.measuresTimeSummativeBasic.statistical.analysisTime.on.learning2021Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
162Design proposal of a personalized Dashboard to optimize teaching-learning in Virtual Learning EnvironmentsDashboard; EVE; LMS; learning; personalized; teachingNoneMethod.developmentNonePerformance.measuresTimeSummativeBasic.statistical.analysisCourse.design2021Quispe, Benjamin Maraza, Apfata, Jhon Edwar Ninasivincha, Figueroa, Ricardo Carlos Qusipe, Solis, Manuel Alejandro Valderrama
163Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approachCross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open educationNoneNon-srl.indicators.identificationotherLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2021Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approachCross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open educationNoneNon-srl.indicators.identificationotherLms.log.dataTrace-quizTransitional.patternProcess.miningLearning.indicators2021Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approachCross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open educationNoneNon-srl.indicators.identificationotherLms.log.dataTrace-forumTransitional.patternProcess.miningLearning.indicators2021Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approachCross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open educationNoneNon-srl.indicators.identificationotherLms.log.dataTrace-feedbackTransitional.patternProcess.miningLearning.indicators2021Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approachCross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open educationNoneNon-srl.indicators.identificationotherLearner.characteristicsEventTransitional.patternProcess.miningLearning.indicators2021Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approachCross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open educationNoneNon-srl.indicators.identificationotherLearner.characteristicsTrace-quizTransitional.patternProcess.miningLearning.indicators2021Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approachCross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open educationNoneNon-srl.indicators.identificationotherLearner.characteristicsTrace-forumTransitional.patternProcess.miningLearning.indicators2021Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approachCross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open educationNoneNon-srl.indicators.identificationotherLearner.characteristicsTrace-feedbackTransitional.patternProcess.miningLearning.indicators2021Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approachCross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open educationNoneNon-srl.indicators.identificationotherPerformance.measuresEventTransitional.patternProcess.miningLearning.indicators2021Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approachCross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open educationNoneNon-srl.indicators.identificationotherPerformance.measuresTrace-quizTransitional.patternProcess.miningLearning.indicators2021Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approachCross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open educationNoneNon-srl.indicators.identificationotherPerformance.measuresTrace-forumTransitional.patternProcess.miningLearning.indicators2021Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
163Impact of cultural diversity on students’ learning behavioral patterns in open and online courses: a lag sequential analysis approachCross-cultural online learning; Hofstede cultural dimensions; MOOCs; lag sequential analysis; open educationNoneNon-srl.indicators.identificationotherPerformance.measuresTrace-feedbackTransitional.patternProcess.miningLearning.indicators2021Tlili, Ahmed, Wang, Huanhuan, Gao, Bojun, Shi, Yihong, Zhiying, Nian, Looi, Chee Kit, Huang, Ronghuai
164Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning ToolEducational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL)NoneExploring.srl.processesSRLCustomized.log.dataEventSummativeProcess.miningLearning.indicators2021Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
164Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning ToolEducational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL)NoneExploring.srl.processesSRLCustomized.log.dataEventSummativeBasic.statistical.analysisLearning.indicators2021Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
164Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning ToolEducational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL)NoneExploring.srl.processesSRLCustomized.log.dataTrace-exerciseSummativeProcess.miningLearning.indicators2021Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
164Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning ToolEducational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL)NoneExploring.srl.processesSRLCustomized.log.dataTrace-exerciseSummativeBasic.statistical.analysisLearning.indicators2021Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
164Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning ToolEducational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL)NoneExploring.srl.processesSRLPerformance.measuresEventSummativeProcess.miningLearning.indicators2021Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
164Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning ToolEducational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL)NoneExploring.srl.processesSRLPerformance.measuresEventSummativeBasic.statistical.analysisLearning.indicators2021Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
164Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning ToolEducational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL)NoneExploring.srl.processesSRLPerformance.measuresTrace-exerciseSummativeProcess.miningLearning.indicators2021Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
164Using Process Mining to Analyze Time Distribution of Self-Assessment and Formative Assessment Exercises on an Online Learning ToolEducational data mining (EDM); Formative assessment (FA); Learning analytics; Online learning; Process.mining (PM); Self-assessment (SA) technologies; Self-regulated learning (SRL)NoneExploring.srl.processesSRLPerformance.measuresTrace-exerciseSummativeBasic.statistical.analysisLearning.indicators2021Dominguez, Cesar, Garcia-Izquierdo, Francisco J., Jaime, Arturo, Perez, Beatriz, Rubio, Angel Luis, Zapata, Maria A.
165Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream dataLearning pathways; Process.mining; Self-regulated learningNoneExploring.srl.processesSRLLms.log.dataEventEvent.sequenceProcess.miningLearning.indicators2021Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream dataLearning pathways; Process.mining; Self-regulated learningNoneExploring.srl.processesSRLLms.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2021Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream dataLearning pathways; Process.mining; Self-regulated learningNoneExploring.srl.processesSRLLms.log.dataEventEvent.sequenceCluster.analysisLearning.indicators2021Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream dataLearning pathways; Process.mining; Self-regulated learningNoneExploring.srl.processesSRLLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2021Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream dataLearning pathways; Process.mining; Self-regulated learningNoneExploring.srl.processesSRLLms.log.dataEventTransitional.patternFrequent.sequence.miningLearning.indicators2021Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream dataLearning pathways; Process.mining; Self-regulated learningNoneExploring.srl.processesSRLLms.log.dataEventTransitional.patternCluster.analysisLearning.indicators2021Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream dataLearning pathways; Process.mining; Self-regulated learningNoneExploring.srl.processesSRLLms.log.dataTrace-exerciseEvent.sequenceProcess.miningLearning.indicators2021Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream dataLearning pathways; Process.mining; Self-regulated learningNoneExploring.srl.processesSRLLms.log.dataTrace-exerciseEvent.sequenceFrequent.sequence.miningLearning.indicators2021Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream dataLearning pathways; Process.mining; Self-regulated learningNoneExploring.srl.processesSRLLms.log.dataTrace-exerciseEvent.sequenceCluster.analysisLearning.indicators2021Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream dataLearning pathways; Process.mining; Self-regulated learningNoneExploring.srl.processesSRLLms.log.dataTrace-exerciseTransitional.patternProcess.miningLearning.indicators2021Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream dataLearning pathways; Process.mining; Self-regulated learningNoneExploring.srl.processesSRLLms.log.dataTrace-exerciseTransitional.patternFrequent.sequence.miningLearning.indicators2021Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream dataLearning pathways; Process.mining; Self-regulated learningNoneExploring.srl.processesSRLLms.log.dataTrace-exerciseTransitional.patternCluster.analysisLearning.indicators2021Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream dataLearning pathways; Process.mining; Self-regulated learningNoneExploring.srl.processesSRLLms.log.dataTrace-otherEvent.sequenceProcess.miningLearning.indicators2021Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream dataLearning pathways; Process.mining; Self-regulated learningNoneExploring.srl.processesSRLLms.log.dataTrace-otherEvent.sequenceFrequent.sequence.miningLearning.indicators2021Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream dataLearning pathways; Process.mining; Self-regulated learningNoneExploring.srl.processesSRLLms.log.dataTrace-otherEvent.sequenceCluster.analysisLearning.indicators2021Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream dataLearning pathways; Process.mining; Self-regulated learningNoneExploring.srl.processesSRLLms.log.dataTrace-otherTransitional.patternProcess.miningLearning.indicators2021Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream dataLearning pathways; Process.mining; Self-regulated learningNoneExploring.srl.processesSRLLms.log.dataTrace-otherTransitional.patternFrequent.sequence.miningLearning.indicators2021Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
165Understanding student learning pathways in traditional online history courses: utilizing process mining analysis on clickstream dataLearning pathways; Process.mining; Self-regulated learningNoneExploring.srl.processesSRLLms.log.dataTrace-otherTransitional.patternCluster.analysisLearning.indicators2021Crosslin, Matt, Breuer, Kimberly, Milikic, Nikola, Dellinger, Justin T.
166Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysisbehavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC)NoneNon-srl.indicators.identificationotherContextualEventTransitional.patternProcess.miningCourse.design2021Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi
166Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysisbehavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC)NoneNon-srl.indicators.identificationotherContextualEventTransitional.patternProcess.miningFeedback2021Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi
166Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysisbehavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC)NoneNon-srl.indicators.identificationotherContextualTrace-forumTransitional.patternProcess.miningCourse.design2021Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi
166Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysisbehavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC)NoneNon-srl.indicators.identificationotherContextualTrace-forumTransitional.patternProcess.miningFeedback2021Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi
166Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysisbehavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC)NoneNon-srl.indicators.identificationotherContextualTrace-feedbackTransitional.patternProcess.miningCourse.design2021Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi
166Smart classroom environments affect teacher-student interaction: Evidence from a behavioural sequence analysisbehavioural sequence; lag sequential analysis; smart classroom (SC); student interaction; teacher; traditional multimedia classroom (TMC)NoneNon-srl.indicators.identificationotherContextualTrace-feedbackTransitional.patternProcess.miningFeedback2021Zhan, Zehui, Wu, Qianyi, Lin, Zhihua, Cai, Jiayi
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataEventEvent.sequenceProcess.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataEventEvent.sequenceCluster.analysisLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataEventTransitional.patternFrequent.sequence.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataEventTransitional.patternCluster.analysisLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataTrace-exerciseEvent.sequenceProcess.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataTrace-exerciseEvent.sequenceFrequent.sequence.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataTrace-exerciseEvent.sequenceCluster.analysisLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataTrace-exerciseTransitional.patternProcess.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataTrace-exerciseTransitional.patternFrequent.sequence.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataTrace-exerciseTransitional.patternCluster.analysisLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataTrace-forumEvent.sequenceProcess.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataTrace-forumEvent.sequenceFrequent.sequence.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataTrace-forumEvent.sequenceCluster.analysisLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataTrace-forumTransitional.patternProcess.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataTrace-forumTransitional.patternFrequent.sequence.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataTrace-forumTransitional.patternCluster.analysisLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataTrace-videoEvent.sequenceProcess.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataTrace-videoEvent.sequenceFrequent.sequence.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataTrace-videoEvent.sequenceCluster.analysisLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataTrace-videoTransitional.patternProcess.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataTrace-videoTransitional.patternFrequent.sequence.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneNon-srl.indicators.identificationotherLms.log.dataTrace-videoTransitional.patternCluster.analysisLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataEventEvent.sequenceProcess.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataEventEvent.sequenceCluster.analysisLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataEventTransitional.patternFrequent.sequence.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataEventTransitional.patternCluster.analysisLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataTrace-exerciseEvent.sequenceProcess.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataTrace-exerciseEvent.sequenceFrequent.sequence.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataTrace-exerciseEvent.sequenceCluster.analysisLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataTrace-exerciseTransitional.patternProcess.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataTrace-exerciseTransitional.patternFrequent.sequence.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataTrace-exerciseTransitional.patternCluster.analysisLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataTrace-forumEvent.sequenceProcess.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataTrace-forumEvent.sequenceFrequent.sequence.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataTrace-forumEvent.sequenceCluster.analysisLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataTrace-forumTransitional.patternProcess.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataTrace-forumTransitional.patternFrequent.sequence.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataTrace-forumTransitional.patternCluster.analysisLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataTrace-videoEvent.sequenceProcess.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataTrace-videoEvent.sequenceFrequent.sequence.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataTrace-videoEvent.sequenceCluster.analysisLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataTrace-videoTransitional.patternProcess.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataTrace-videoTransitional.patternFrequent.sequence.miningLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
167Putting It All Together: Combining Learning Analytics Methods and Data Sources to Understand Students’ Approaches to Learning Programmingautomated assessment; computer science; learning analytics; process mining; programming; sequence miningNoneMethod.developmentotherLms.log.dataTrace-videoTransitional.patternCluster.analysisLearning.indicators2021Lopez‚Äêpernas, Sonsoles, Saqr, Mohammed, Viberg, Olga
168Using Three Social Network Analysis Approaches to Understand Computer-Supported Collaborative Learningcomputer-supported collaborative learning; multi-mode Network analysiss; relational ties; social learning analytics; social Network analysis analysisNoneNon-srl.indicators.identificationcollaborative knowledge buildingLearning.productTrace-forumOther.sequential.patternsNetwork.analysisCollaboration2021Ouyang, Fan
168Using Three Social Network Analysis Approaches to Understand Computer-Supported Collaborative Learningcomputer-supported collaborative learning; multi-mode Network analysiss; relational ties; social learning analytics; social Network analysis analysisNoneMethod.developmentcollaborative knowledge buildingLearning.productTrace-forumOther.sequential.patternsNetwork.analysisCollaboration2021Ouyang, Fan
169Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasksLongest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence miningNoneNon-srl.indicators.identificationotherCustomized.log.dataEventEvent.sequenceBasic.statistical.analysisLearning.indicators2021He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasksLongest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence miningNoneNon-srl.indicators.identificationotherCustomized.log.dataEventSummativeBasic.statistical.analysisLearning.indicators2021He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasksLongest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence miningNoneNon-srl.indicators.identificationotherCustomized.log.dataTrace-otherEvent.sequenceBasic.statistical.analysisLearning.indicators2021He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasksLongest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence miningNoneNon-srl.indicators.identificationotherCustomized.log.dataTrace-otherSummativeBasic.statistical.analysisLearning.indicators2021He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasksLongest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence miningNoneNon-srl.indicators.identificationotherPerformance.measuresEventEvent.sequenceBasic.statistical.analysisLearning.indicators2021He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasksLongest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence miningNoneNon-srl.indicators.identificationotherPerformance.measuresEventSummativeBasic.statistical.analysisLearning.indicators2021He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasksLongest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence miningNoneNon-srl.indicators.identificationotherPerformance.measuresTrace-otherEvent.sequenceBasic.statistical.analysisLearning.indicators2021He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasksLongest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence miningNoneNon-srl.indicators.identificationotherPerformance.measuresTrace-otherSummativeBasic.statistical.analysisLearning.indicators2021He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasksLongest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence miningNoneMethod.developmentotherCustomized.log.dataEventEvent.sequenceBasic.statistical.analysisLearning.indicators2021He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasksLongest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence miningNoneMethod.developmentotherCustomized.log.dataEventSummativeBasic.statistical.analysisLearning.indicators2021He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasksLongest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence miningNoneMethod.developmentotherCustomized.log.dataTrace-otherEvent.sequenceBasic.statistical.analysisLearning.indicators2021He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasksLongest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence miningNoneMethod.developmentotherCustomized.log.dataTrace-otherSummativeBasic.statistical.analysisLearning.indicators2021He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasksLongest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence miningNoneMethod.developmentotherPerformance.measuresEventEvent.sequenceBasic.statistical.analysisLearning.indicators2021He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasksLongest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence miningNoneMethod.developmentotherPerformance.measuresEventSummativeBasic.statistical.analysisLearning.indicators2021He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasksLongest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence miningNoneMethod.developmentotherPerformance.measuresTrace-otherEvent.sequenceBasic.statistical.analysisLearning.indicators2021He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
169Leveraging process data to assess adults’ problem-solving skills: Using sequence mining to identify behavioral patterns across digital tasksLongest common subsequence; PIAAC; Problem-solving skills; Process data; Sequence miningNoneMethod.developmentotherPerformance.measuresTrace-otherSummativeBasic.statistical.analysisLearning.indicators2021He, Qiwei, Borgonovi, Francesca, Paccagnella, Marco
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataEventEvent.sequenceProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataEventEvent.sequenceCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataEventEvent.sequenceVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataEventTransitional.patternFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataEventTransitional.patternCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataEventTransitional.patternVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataEventGroup.event.patternProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataEventGroup.event.patternFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataEventGroup.event.patternCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataEventGroup.event.patternVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataTimeEvent.sequenceProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataTimeEvent.sequenceFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataTimeEvent.sequenceCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataTimeEvent.sequenceVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataTimeTransitional.patternProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataTimeTransitional.patternFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataTimeTransitional.patternCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataTimeTransitional.patternVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataTimeGroup.event.patternProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataTimeGroup.event.patternFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataTimeGroup.event.patternCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherLms.log.dataTimeGroup.event.patternVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresEventEvent.sequenceProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresEventEvent.sequenceFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresEventEvent.sequenceCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresEventEvent.sequenceVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresEventTransitional.patternProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresEventTransitional.patternFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresEventTransitional.patternCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresEventTransitional.patternVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresEventGroup.event.patternProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresEventGroup.event.patternFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresEventGroup.event.patternCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresEventGroup.event.patternVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresTimeEvent.sequenceProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresTimeEvent.sequenceFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresTimeEvent.sequenceCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresTimeEvent.sequenceVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresTimeTransitional.patternProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresTimeTransitional.patternFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresTimeTransitional.patternCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresTimeTransitional.patternVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresTimeGroup.event.patternProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresTimeGroup.event.patternFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresTimeGroup.event.patternCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneNon-srl.indicators.identificationotherPerformance.measuresTimeGroup.event.patternVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataEventEvent.sequenceProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataEventEvent.sequenceCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataEventEvent.sequenceVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataEventTransitional.patternProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataEventTransitional.patternFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataEventTransitional.patternCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataEventTransitional.patternVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataEventGroup.event.patternProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataEventGroup.event.patternFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataEventGroup.event.patternCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataEventGroup.event.patternVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataTimeEvent.sequenceProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataTimeEvent.sequenceFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataTimeEvent.sequenceCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataTimeEvent.sequenceVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataTimeTransitional.patternProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataTimeTransitional.patternFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataTimeTransitional.patternCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataTimeTransitional.patternVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataTimeGroup.event.patternProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataTimeGroup.event.patternFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataTimeGroup.event.patternCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherLms.log.dataTimeGroup.event.patternVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresEventEvent.sequenceProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresEventEvent.sequenceFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresEventEvent.sequenceCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresEventEvent.sequenceVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresEventTransitional.patternProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresEventTransitional.patternFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresEventTransitional.patternCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresEventTransitional.patternVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresEventGroup.event.patternProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresEventGroup.event.patternFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresEventGroup.event.patternCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresEventGroup.event.patternVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresTimeEvent.sequenceProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresTimeEvent.sequenceFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresTimeEvent.sequenceCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresTimeEvent.sequenceVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresTimeTransitional.patternProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresTimeTransitional.patternFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresTimeTransitional.patternCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresTimeTransitional.patternVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresTimeGroup.event.patternProcess.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresTimeGroup.event.patternFrequent.sequence.miningLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresTimeGroup.event.patternCluster.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
170The longitudinal trajectories of online engagement over a full programLearning analytics; Longitudinal engagement; Sequence mining; Survival analysis; Trajectories of engagementNoneMethod.developmentotherPerformance.measuresTimeGroup.event.patternVisualization.analysisLearning.indicators2021Saqr, Mohammed, Lopez-Pernas, Sonsoles
171Visual search patterns, information selection strategies, and information anxiety for online information problem solvingData science applications in education; Eye-tracking; Human computer interaction; Information literacy; Teaching/learning strategiesNoneNon-srl.indicators.identificationotherMultimodalEventTransitional.patternProcess.miningLearning.indicators2021Tsai, Meng Jung, Wu, An Hsuan
171Visual search patterns, information selection strategies, and information anxiety for online information problem solvingData science applications in education; Eye-tracking; Human computer interaction; Information literacy; Teaching/learning strategiesNoneNon-srl.indicators.identificationotherMultimodalTrace-otherTransitional.patternProcess.miningLearning.indicators2021Tsai, Meng Jung, Wu, An Hsuan
172Predicting learner’s performance through video sequences viewing behavior analysis using educational data-miningEducational data mining; Educational video; Pedagogical sequences; Performance prediction; Video viewing behaviorNoneMethod.developmentNoneLms.log.dataEventSummativeOther.predictions.modelsNo.learning.focus.outcome2021El Aouifi}, Houssam, {El Hajji}, Mohamed, Es-Saady, Youssef, Douzi, Hassan
172Predicting learner’s performance through video sequences viewing behavior analysis using educational data-miningEducational data mining; Educational video; Pedagogical sequences; Performance prediction; Video viewing behaviorNoneMethod.developmentNoneLms.log.dataTrace-videoSummativeOther.predictions.modelsNo.learning.focus.outcome2021El Aouifi}, Houssam, {El Hajji}, Mohamed, Es-Saady, Youssef, Douzi, Hassan
172Predicting learner’s performance through video sequences viewing behavior analysis using educational data-miningEducational data mining; Educational video; Pedagogical sequences; Performance prediction; Video viewing behaviorNoneMethod.developmentNonePerformance.measuresEventSummativeOther.predictions.modelsNo.learning.focus.outcome2021El Aouifi}, Houssam, {El Hajji}, Mohamed, Es-Saady, Youssef, Douzi, Hassan
172Predicting learner’s performance through video sequences viewing behavior analysis using educational data-miningEducational data mining; Educational video; Pedagogical sequences; Performance prediction; Video viewing behaviorNoneMethod.developmentNonePerformance.measuresTrace-videoSummativeOther.predictions.modelsNo.learning.focus.outcome2021El Aouifi}, Houssam, {El Hajji}, Mohamed, Es-Saady, Youssef, Douzi, Hassan
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneExploring.srl.processesSRLLms.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneExploring.srl.processesSRLLms.log.dataEventEvent.sequenceCluster.analysisLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneExploring.srl.processesSRLLms.log.dataEventGroup.event.patternFrequent.sequence.miningLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneExploring.srl.processesSRLLms.log.dataEventGroup.event.patternCluster.analysisLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneExploring.srl.processesSRLLms.log.dataTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneExploring.srl.processesSRLLms.log.dataTrace-quizEvent.sequenceCluster.analysisLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneExploring.srl.processesSRLLms.log.dataTrace-quizGroup.event.patternFrequent.sequence.miningLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneExploring.srl.processesSRLLms.log.dataTrace-quizGroup.event.patternCluster.analysisLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneExploring.srl.processesSRLPerformance.measuresEventEvent.sequenceFrequent.sequence.miningLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneExploring.srl.processesSRLPerformance.measuresEventEvent.sequenceCluster.analysisLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneExploring.srl.processesSRLPerformance.measuresEventGroup.event.patternFrequent.sequence.miningLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneExploring.srl.processesSRLPerformance.measuresEventGroup.event.patternCluster.analysisLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneExploring.srl.processesSRLPerformance.measuresTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneExploring.srl.processesSRLPerformance.measuresTrace-quizEvent.sequenceCluster.analysisLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneExploring.srl.processesSRLPerformance.measuresTrace-quizGroup.event.patternFrequent.sequence.miningLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneExploring.srl.processesSRLPerformance.measuresTrace-quizGroup.event.patternCluster.analysisLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneGroup.comparisonSRLLms.log.dataEventEvent.sequenceFrequent.sequence.miningLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneGroup.comparisonSRLLms.log.dataEventEvent.sequenceCluster.analysisLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneGroup.comparisonSRLLms.log.dataEventGroup.event.patternFrequent.sequence.miningLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneGroup.comparisonSRLLms.log.dataEventGroup.event.patternCluster.analysisLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneGroup.comparisonSRLLms.log.dataTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneGroup.comparisonSRLLms.log.dataTrace-quizEvent.sequenceCluster.analysisLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneGroup.comparisonSRLLms.log.dataTrace-quizGroup.event.patternFrequent.sequence.miningLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneGroup.comparisonSRLLms.log.dataTrace-quizGroup.event.patternCluster.analysisLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneGroup.comparisonSRLPerformance.measuresEventEvent.sequenceFrequent.sequence.miningLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneGroup.comparisonSRLPerformance.measuresEventEvent.sequenceCluster.analysisLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneGroup.comparisonSRLPerformance.measuresEventGroup.event.patternFrequent.sequence.miningLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneGroup.comparisonSRLPerformance.measuresEventGroup.event.patternCluster.analysisLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneGroup.comparisonSRLPerformance.measuresTrace-quizEvent.sequenceFrequent.sequence.miningLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneGroup.comparisonSRLPerformance.measuresTrace-quizEvent.sequenceCluster.analysisLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneGroup.comparisonSRLPerformance.measuresTrace-quizGroup.event.patternFrequent.sequence.miningLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
173Diagnosing virtual patients in a technology-rich learning environment: a sequential Mining of Students’ efficiency and behavioral patternsClinical reasoning; Efficiency; Metacognition; Self-regulated learning; Sequential miningNoneGroup.comparisonSRLPerformance.measuresTrace-quizGroup.event.patternCluster.analysisLearning.indicators2021Zheng, Juan, Li, Shan, Lajoie, Susanne P.
174Learner behavior prediction in a learning management systemCognitive style; Learner behavior; Learner modeling; Learning management system; Learning style; Machine learning; Neural Network analysisNoneAt-risk.student.identificationNoneLms.log.dataEventOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2021Lwande, Charles, Oboko, Robert, Muchemi, Lawrence
174Learner behavior prediction in a learning management systemCognitive style; Learner behavior; Learner modeling; Learning management system; Learning style; Machine learning; Neural Network analysisNoneAt-risk.student.identificationNoneLms.log.dataTimeOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2021Lwande, Charles, Oboko, Robert, Muchemi, Lawrence
175Predictive learning analytics using deep learning model in MOOCs’ courses videosDeep learning (LSTM); MOOCs courses; Prediction; Video-clickstreamNoneAt-risk.student.identificationNoneLms.log.dataEventSummativeOther.predictions.modelsNo.learning.focus.outcome2021Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M.
175Predictive learning analytics using deep learning model in MOOCs’ courses videosDeep learning (LSTM); MOOCs courses; Prediction; Video-clickstreamNoneAt-risk.student.identificationNoneLms.log.dataEventOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2021Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M.
175Predictive learning analytics using deep learning model in MOOCs’ courses videosDeep learning (LSTM); MOOCs courses; Prediction; Video-clickstreamNoneAt-risk.student.identificationNoneLms.log.dataTimeSummativeOther.predictions.modelsNo.learning.focus.outcome2021Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M.
175Predictive learning analytics using deep learning model in MOOCs’ courses videosDeep learning (LSTM); MOOCs courses; Prediction; Video-clickstreamNoneAt-risk.student.identificationNoneLms.log.dataTimeOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2021Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M.
175Predictive learning analytics using deep learning model in MOOCs’ courses videosDeep learning (LSTM); MOOCs courses; Prediction; Video-clickstreamNoneAt-risk.student.identificationNoneLms.log.dataTrace-videoSummativeOther.predictions.modelsNo.learning.focus.outcome2021Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M.
175Predictive learning analytics using deep learning model in MOOCs’ courses videosDeep learning (LSTM); MOOCs courses; Prediction; Video-clickstreamNoneAt-risk.student.identificationNoneLms.log.dataTrace-videoOther.sequential.patternsOther.predictions.modelsNo.learning.focus.outcome2021Mubarak, Ahmed Ali, Cao, Han, Ahmed, Salah A.M.
176Understanding students’ behavioural intention to use facebook as a supplementary learning platform: A mixed methods approachFacebook; Mixed methods; Online supplementary learning platform; Perceived enjoyment; Technology acceptanceNoneNon-srl.indicators.identificationaffective learningContextualEventTransitional.patternBasic.statistical.analysisLearning.indicators2021Hoi, Vo Ngoc, Hang, Ho Le